indexing study1.csv [=========================================================] 20.92GB/s, eta:  0s                                                                                                   
  2   3   5 
216 218 216 
                       Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment2)   2  18.28   9.141   41.17 <2e-16 ***
Residuals             645 143.22   0.222                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Independence ~ as.factor(treatment2), data = study1)

$`as.factor(treatment2)`
           diff         lwr        upr     p adj
3-2  0.04013761 -0.06613354  0.1464088 0.6485595
5-2 -0.33528037 -0.44204469 -0.2285161 0.0000000
5-3 -0.37541799 -0.48193829 -0.2688977 0.0000000

  treatment2   N Obedience        sd         se         ci
1          2 216  1.574074 0.4956312 0.03372343 0.06647088
2          3 217  1.525346 0.5005118 0.03397695 0.06696883
3          5 215  1.855814 0.3520981 0.02401289 0.04733208
                      Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)   2  13.72   6.862   33.16 1.95e-14 ***
Residuals            645 133.46   0.207                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Obedience ~ as.factor(treatment), data = study1)

$`as.factor(treatment)`
           diff        lwr        upr     p adj
3-2 -0.04872845 -0.1514315 0.05397461 0.5054019
5-2  0.28173988  0.1787988 0.38468096 0.0000000
5-3  0.33046833  0.2276456 0.43329102 0.0000000

  treatment2   N Curiosity        sd         se         ci
1          2 213  1.727700 0.4461923 0.03057260 0.06026522
2          3 218  1.610092 0.4888517 0.03310922 0.06525682
3          5 216  1.495370 0.5011400 0.03409826 0.06720968
                      Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)   2   5.79  2.8944   12.59 4.33e-06 ***
Residuals            644 148.06  0.2299                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
3 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Curiosity ~ as.factor(treatment), data = study1)

$`as.factor(treatment)`
          diff        lwr          upr     p adj
3-2 -0.1176078 -0.2261267 -0.009088911 0.0299049
5-2 -0.2323292 -0.3410960 -0.123562280 0.0000020
5-3 -0.1147214 -0.2228584 -0.006584341 0.0345429

  treatment2   N Considerate        sd         se         ci
1          2 214    1.355140 0.4796778 0.03279010 0.06463466
2          3 218    1.403670 0.4917619 0.03330632 0.06564530
3          5 216    1.259259 0.4392461 0.02988691 0.05890887
                      Df Sum Sq Mean Sq F value  Pr(>F)   
as.factor(treatment)   2   2.34  1.1704    5.28 0.00531 **
Residuals            645 142.97  0.2217                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Considerate ~ as.factor(treatment), data = study1)

$`as.factor(treatment)`
           diff         lwr         upr     p adj
3-2  0.04852954 -0.05789793  0.15495700 0.5323405
5-2 -0.09588093 -0.20255219  0.01079034 0.0882862
5-3 -0.14441047 -0.25058900 -0.03823193 0.0041817

RStudioGD 
        2 
  [1] 3 5 2 5 2 2 2 5 3 2 5 2 2 3 3 2 3 5 3 5 3 3 3 3 2 3 3 3 5 3 5 5 2 2 3 3 5 2 2 2 5 5 2 5 2 5 3
 [48] 2 5 2 5 5 5 2 5 5 5 5 2 3 2 2 5 3 3 5 2 2 3 5 5 3 2 5 2 3 3 5 2 3 2 2 5 3 3 5 2 3 3 3 5 5 5 2
 [95] 3 5 5 5 5 2 3 5 3 5 2 5 5 5 5 5 5 2 5 3 5 3 5 2 5 3 5 5 3 2 2 5 3 5 5 3 5 3 3 5 3 5 2 2 2 3 2
[142] 2 5 3 2 5 3 2 2 2 5 5 2 5 2 5 5 2 2 5 5 5 5 5 5 3 3 5 2 3 2 5 5 2 3 5 2 3 3 3 3 3 3 2 2 3 5 5
[189] 2 3 2 5 5 3 2 5 3 5 3 5 3 3 5 3 5 3 2 2 3 2 5 3 3 3 3 5 2 3 2 5 2 5 2 2 3 3 3 2 5 2 2 5 2 5 5
[236] 5 3 3 2 3 5 5 3 3 5 2 2 3 2 5 2 3 5 5 5 5 3 3 5 2 3 5 2 2 2 2 5 3 3 5 5 5 2 2 3 3 2 2 5 2 5 2
[283] 5 2 2 2 2 3 5 5 2 2 5 3 3 5 5 5 3 3 5 3 5 5 5 2 3 2 2 5 3 5 3 3 5 2 5 2 3 3 5 3 3 2 3 2 5 2 2
[330] 2 2 5 2 5 3 2 2 2 5 3 2 5 3 2 3 3 2 2 2 3 5 5 2 2 2 5 2 2 3 3 3 2 5 2 5 5 2 3 5 3 3 2 2 3 2 3
[377] 2 3 2 5 3 2 3 3 2 3 2 2 2 5 5 2 5 5 5 3 2 5 5 5 5 5 2 3 2 5 3 3 2 5 2 3 5 5 2 5 5 2 2 3 3 5 3
[424] 3 2 3 3 2 5 2 2 3 2 3 3 2 3 3 3 3 3 5 3 2 3 3 2 5 3 3 3 3 5 3 3 2 2 3 3 5 3 3 2 5 5 2 3 3 2 2
[471] 3 2 3 5 3 2 2 2 2 2 2 5 3 5 3 2 5 2 3 2 5 5 5 3 3 2 2 2 5 5 5 3 5 3 5 5 2 3 2 2 2 2 3 3 2 2 2
[518] 2 3 5 3 2 2 3 5 2 5 3 3 2 2 5 3 2 5 2 5 2 3 2 5 2 2 3 3 2 3 3 3 5 3 2 5 5 3 5 2 5 5 5 5 2 2 5
[565] 5 5 5 5 3 5 2 2 3 3 2 5 2 3 2 5 3 5 5 5 3 3 2 3 2 5 3 2 3 3 2 3 5 3 3 3 2 2 3 3 3 5 2 3 5 2 3
[612] 5 3 3 2 5 3 5 5 2 2 3 5 3 3 2 2 2 2 5 5 3 5 2 3 3 3 5 2 3 2 3 3 5 5 3 2 5 3 3
Levels: 2 3 5
                      Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment)   2  134.8   67.41   44.29 <2e-16 ***
Residuals            641  975.7    1.52                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
6 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = auth ~ as.factor(treatment), data = study1)

$`as.factor(treatment)`
           diff        lwr        upr     p adj
3-2  0.01583696 -0.2637118  0.2953857 0.9902832
5-2 -0.96327498 -1.2437926 -0.6827573 0.0000000
5-3 -0.97911193 -1.2583308 -0.6998930 0.0000000

RStudioGD 
        2 
indexed 0B in  0s, 0B/sindexed 1.00TB in  0s, 267.73TB/s                                                                                                 
   1    2 
 607 1082 

                               Boy Controlcondition--nogenderquestion 
                               564                                565 
                              Girl 
                               563 

                               Boy Controlcondition--nogenderquestion 
                               564                                565 
                              Girl 
                               563 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    1.00    2.00    1.55    2.00    2.00 
                        Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment2)    2   53.6  26.785     124 <2e-16 ***
Residuals             1681  363.1   0.216                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Independence ~ as.factor(treatment2), data = study2)

$`as.factor(treatment2)`
                                               diff         lwr        upr     p adj
Controlcondition--nogenderquestion-Boy   0.07780885  0.01270977  0.1429079 0.0141397
Girl-Boy                                -0.33322318 -0.39832226 -0.2681241 0.0000000
Girl-Controlcondition--nogenderquestion -0.41103203 -0.47607306 -0.3459910 0.0000000

                          treatment2   N Obedience        sd         se         ci     obed2
1                                Boy 560  1.591071 0.4920756 0.02079399 0.04084390 0.4089286
2 Controlcondition--nogenderquestion 562  1.480427 0.5000618 0.02109384 0.04143255 0.5195730
3                               Girl 562  1.850534 0.3568650 0.01505344 0.02956800 0.1494662
     test
1     Boy
2 Control
3    Girl
                       Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment)    2   40.6  20.280   98.22 <2e-16 ***
Residuals            1681  347.1   0.206                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Obedience ~ as.factor(treatment), data = study2)

$`as.factor(treatment)`
                                              diff        lwr        upr     p adj
Controlcondition--nogenderquestion-Boy  -0.1106444 -0.1742882 -0.0470006 0.0001405
Girl-Boy                                 0.2594624  0.1958186  0.3231062 0.0000000
Girl-Controlcondition--nogenderquestion  0.3701068  0.3065197  0.4336938 0.0000000

                          treatment2   N Curiosity        sd         se         ci
1                                Boy 560  1.651786 0.4768303 0.02014976 0.03957849
2 Controlcondition--nogenderquestion 562  1.638790 0.4807792 0.02028045 0.03983488
3                               Girl 562  1.540925 0.4987662 0.02103918 0.04132520
                       Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)    2    4.1  2.0624   8.747 0.000166 ***
Residuals            1681  396.3  0.2358                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Curiosity ~ as.factor(treatment), data = study2)

$`as.factor(treatment)`
                                               diff         lwr         upr     p adj
Controlcondition--nogenderquestion-Boy  -0.01299568 -0.08100483  0.05501347 0.8951869
Girl-Boy                                -0.11086045 -0.17886959 -0.04285130 0.0004002
Girl-Controlcondition--nogenderquestion -0.09786477 -0.16581327 -0.02991626 0.0021506

                          treatment2   N Considerate        sd         se         ci
1                                Boy 560    1.310714 0.4631998 0.01957376 0.03844712
2 Controlcondition--nogenderquestion 562    1.379004 0.4855712 0.02048259 0.04023193
3                               Girl 562    1.240214 0.4275936 0.01803695 0.03542820
                       Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)    2    5.4  2.7066   12.82 2.97e-06 ***
Residuals            1681  354.8  0.2111                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Considerate ~ as.factor(treatment), data = study2)

$`as.factor(treatment)`
                                               diff          lwr          upr     p adj
Controlcondition--nogenderquestion-Boy   0.06828927  0.003943923  0.132634623 0.0344156
Girl-Boy                                -0.07050076 -0.134846113 -0.006155412 0.0276553
Girl-Controlcondition--nogenderquestion -0.13879004 -0.203078011 -0.074502060 0.0000014

RStudioGD 
        2 
RStudioGD 
        2 
indexing study3.csv [========================================================] 154.27GB/s, eta:  0s                                                                                                   
   0    1 
2066  277 

   0    1 
 527 1919 

   0    1 
2137  309 

  1   2   3   4   5   6   7   8   9  10  11  12  13 
215 243 331 257 240 229 135 144  83 107 259 137  64 

   0    1 
1342 1101 

   0    1 
1019 1421 

   0    1 
1414 1032 

   0    1 
1338 1108 

  1   2   3 
386 310 722 

   0    1 
1872  574 

  0   1   2   3   4   5 
838 612 235 274 335 147 

  1   2 
670 281 

  0   1   2   3   4   5   6 
670 282 414  19 329 272 458 

   0    1    2 
1304 1139    3 

  1   2   3 
811 814 821 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.2347  0.0000  1.0000 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.000   3.000   5.723  11.000  12.000       2 

  1   2   3 
811 814 821 

  1   2   3 
811 814 821 
  treatment2   N   Respect        sd         se         ci    test
1          1 811 0.6017263 0.4898445 0.01720077 0.03376333 Control
2          2 813 0.6383764 0.4807663 0.01686121 0.03309670     Boy
3          3 818 0.3740831 0.4841813 0.01692900 0.03322946    Girl
                       Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment)    2   33.5  16.732   71.15 <2e-16 ***
Residuals            2439  573.6   0.235                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
4 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Respect ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
           diff         lwr         upr     p adj
2-1  0.03665012 -0.01979063  0.09309087 0.2803301
3-1 -0.22764313 -0.28399768 -0.17128859 0.0000000
3-2 -0.26429325 -0.32061298 -0.20797353 0.0000000

# Effect Size for ANOVA (Type I)

Parameter            | Eta2 |       95% CI
------------------------------------------
as.factor(treatment) | 0.06 | [0.04, 1.00]

- One-sided CIs: upper bound fixed at [1.00].  treatment2   N  Obedient        sd         se         ci
1          1 810 0.4641975 0.4990247 0.01753394 0.03441738
2          2 813 0.3653137 0.4818144 0.01689797 0.03316885
3          3 821 0.2058465 0.4045653 0.01411943 0.02771448
                       Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment)    2   27.7  13.871   64.59 <2e-16 ***
Residuals            2441  524.2   0.215                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Obedient ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
           diff        lwr         upr    p adj
2-1 -0.09888388 -0.1528341 -0.04493361 5.31e-05
3-1 -0.25835100 -0.3121699 -0.20453208 0.00e+00
3-2 -0.15946712 -0.2132360 -0.10569821 0.00e+00

# Effect Size for ANOVA (Type I)

Parameter            | Eta2 |       95% CI
------------------------------------------
as.factor(treatment) | 0.05 | [0.04, 1.00]

- One-sided CIs: upper bound fixed at [1.00].  treatment2   N GoodMannered        sd         se         ci
1          1 810    0.5691358 0.4955031 0.01741021 0.03417450
2          2 810    0.6901235 0.4627282 0.01625861 0.03191404
3          3 817    0.5752754 0.4946039 0.01730400 0.03396560
                       Df Sum Sq Mean Sq F value  Pr(>F)    
as.factor(treatment)    2    7.5   3.766   16.04 1.2e-07 ***
Residuals            2434  571.5   0.235                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
9 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = GoodMannered ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
            diff         lwr         upr     p adj
2-1  0.120987654  0.06452292  0.17745239 0.0000016
3-1  0.006139595 -0.05020407  0.06248326 0.9646428
3-2 -0.114848059 -0.17119172 -0.05850440 0.0000055

  treatment2   N WellBehaved        sd         se         ci
1          1 808   0.3824257 0.4862807 0.01710730 0.03358005
2          2 811   0.3612824 0.4806686 0.01687856 0.03313087
3          3 818   0.2652812 0.4417529 0.01544553 0.03031759
                       Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)    2    6.4   3.175   14.38 6.17e-07 ***
Residuals            2434  537.4   0.221                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
9 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = WellBehaved ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
           diff         lwr         upr     p adj
2-1 -0.02114338 -0.07591653  0.03362978 0.6370185
3-1 -0.11714457 -0.17180064 -0.06248850 0.0000016
3-2 -0.09600119 -0.15060639 -0.04139600 0.0001144

  treatment2   N    Polite        sd         se         ci
1          1 811 0.7225647 0.4480094 0.01573174 0.03087978
2          2 811 0.7583231 0.4283637 0.01504188 0.02952567
3          3 816 0.5894608 0.4922334 0.01723161 0.03382357
                       Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)    2   12.9   6.447   30.86 5.81e-14 ***
Residuals            2435  508.7   0.209                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
8 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Polite ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
           diff         lwr         upr     p adj
2-1  0.03575832 -0.01747024  0.08898689 0.2564848
3-1 -0.13310395 -0.18625092 -0.07995699 0.0000000
3-2 -0.16886227 -0.22200924 -0.11571531 0.0000000

  treatment2   N   Orderly        sd         se         ci
1          1 806 0.2791563 0.4488631 0.01581053 0.03103472
2          2 812 0.3633005 0.4812468 0.01688845 0.03315023
3          3 818 0.3105134 0.4629869 0.01618796 0.03177489
                       Df Sum Sq Mean Sq F value  Pr(>F)   
as.factor(treatment)    2    2.9  1.4641   6.783 0.00115 **
Residuals            2433  525.1  0.2158                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
10 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Orderly ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
           diff         lwr         upr     p adj
2-1  0.08414417  0.02997138 0.138316946 0.0008047
3-1  0.03135712 -0.02271660 0.085430840 0.3622845
3-2 -0.05278705 -0.10676004 0.001185953 0.0568730

  treatment2   N Disciplined        sd         se         ci
1          1 808   0.4467822 0.4974677 0.01750085 0.03435256
2          2 813   0.4870849 0.5001409 0.01754071 0.03443047
3          3 817   0.2974296 0.4574072 0.01600265 0.03141121
                       Df Sum Sq Mean Sq F value  Pr(>F)    
as.factor(treatment)    2   16.3   8.139   34.55 1.6e-15 ***
Residuals            2435  573.6   0.236                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
8 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Disciplined ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
           diff         lwr         upr     p adj
2-1  0.04030269 -0.01623593  0.09684132 0.2163219
3-1 -0.14935256 -0.20582215 -0.09288296 0.0000000
3-2 -0.18965525 -0.24603747 -0.13327303 0.0000000

  treatment2   N     Loyal        sd         se         ci
1          1 811 0.4180025 0.4935349 0.01733036 0.03401770
2          2 813 0.5166052 0.5000318 0.01753688 0.03442297
3          3 819 0.4639805 0.4990056 0.01743667 0.03422588
                       Df Sum Sq Mean Sq F value   Pr(>F)    
as.factor(treatment)    2      4  1.9768   7.986 0.000349 ***
Residuals            2440    604  0.2475                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
3 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Loyal ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
          diff         lwr         upr     p adj
2-1  0.0986027  0.04069553 0.156509875 0.0001981
3-1  0.0459780 -0.01182315 0.103779149 0.1490327
3-2 -0.0526247 -0.11039012 0.005140715 0.0828204

RStudioGD 
        2 
RStudioGD 
        2 
  treatment2   N   Author       sd         se        ci
1          1 798 3.887218 2.343696 0.08296594 0.1628576
2          2 805 4.178882 2.211526 0.07794605 0.1530018
3          3 805 3.068323 2.206741 0.07777739 0.1526707
RStudioGD 
        2 
                       Df Sum Sq Mean Sq F value Pr(>F)    
as.factor(treatment)    2    533  266.75   52.48 <2e-16 ***
Residuals            2405  12225    5.08                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
38 observations deleted due to missingness
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Author ~ as.factor(treatment), data = limited)

$`as.factor(treatment)`
          diff         lwr        upr     p adj
2-1  0.2916639  0.02753754  0.5557903 0.0261453
3-1 -0.8188951 -1.08302146 -0.5547687 0.0000000
3-2 -1.1105590 -1.37410808 -0.8470099 0.0000000


1.0 2.0 3.0 1.1 2.1 3.1 
472 477 465 339 337 356 
  treatment   N   Author       sd        se        ci    test
1         1 334 3.952096 2.336056 0.1278233 0.2514429 Control
2         2 334 4.185629 2.243771 0.1227737 0.2415098     Boy
3         3 352 3.289773 2.134003 0.1137427 0.2237030    Girl
  treatment   N   Author       sd        se        ci    test
1         1 464 3.840517 2.350588 0.1091233 0.2144384 Control
2         2 471 4.174098 2.190756 0.1009447 0.1983588     Boy
3         3 453 2.896247 2.248954 0.1056651 0.2076558    Girl
  treatment   N   Author       sd        se        ci    test
1         1 383 4.182768 2.297965 0.1174205 0.2308715 Control
2         2 390 4.323077 2.264793 0.1146822 0.2254746     Boy
3         3 383 3.383812 2.298747 0.1174605 0.2309501    Girl
  treatment   N   Author       sd        se        ci    test
1         1 415 3.614458 2.355133 0.1156089 0.2272537 Control
2         2 415 4.043373 2.154211 0.1057461 0.2078662     Boy
3         3 422 2.781991 2.081443 0.1013231 0.1991621    Girl
RStudioGD 
        2 

Call:
lm(formula = Author ~ Treatment * hostile_sexism + Treatment * 
    ben_sexism + Treatment * ben_men + Treatment * hostil_men, 
    data = limited)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.4239 -1.4836 -0.0812  1.3895  5.5046 

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   0.418807   0.352842   1.187 0.235371    
TreatmentBoy                  0.293977   0.509294   0.577 0.563844    
TreatmentGirl                -0.373517   0.525966  -0.710 0.477682    
hostile_sexism                0.049283   0.013064   3.773 0.000166 ***
ben_sexism                    0.091947   0.012746   7.214 7.40e-13 ***
ben_men                       0.131363   0.028528   4.605 4.36e-06 ***
hostil_men                   -0.063061   0.023890  -2.640 0.008355 ** 
TreatmentBoy:hostile_sexism  -0.040826   0.018660  -2.188 0.028778 *  
TreatmentGirl:hostile_sexism  0.041651   0.018447   2.258 0.024046 *  
TreatmentBoy:ben_sexism       0.007937   0.018081   0.439 0.660709    
TreatmentGirl:ben_sexism     -0.023788   0.017868  -1.331 0.183204    
TreatmentBoy:ben_men         -0.022125   0.041392  -0.535 0.593030    
TreatmentGirl:ben_men         0.035271   0.040104   0.879 0.379230    
TreatmentBoy:hostil_men       0.040840   0.034648   1.179 0.238635    
TreatmentGirl:hostil_men     -0.071380   0.035115  -2.033 0.042196 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.017 on 2266 degrees of freedom
  (165 observations deleted due to missingness)
Multiple R-squared:  0.2446,	Adjusted R-squared:  0.2399 
F-statistic:  52.4 on 14 and 2266 DF,  p-value: < 2.2e-16

RStudioGD 
        2 
################### Nested Model Comparison #########################

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

lavaan->unknown():  
   lavaan NOTE: The “Chisq” column contains standard test statistics, not the robust test that 
   should be reported per model. A robust difference test is a function of two standard (not 
   robust) statistics.
                    Df AIC BIC   Chisq Chisq diff Df diff Pr(>Chisq)    
meq.list.configural  6          11.173                                  
meq.list.strong     10          36.205     28.189       4  1.142e-05 ***
meq.list.strict     20         184.215    133.412      10  < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

####################### Model Fit Indices ###########################
                    chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled tli.scaled  srmr
meq.list.configural      16.664†         6          .011        .091†      .964†      .892† .058†
meq.list.strong          46.751         10          .000        .131       .876       .777  .086 
meq.list.strict         192.205         20          .000        .201       .419       .477  .076 

################## Differences in Fit Indices #######################
                                      df.scaled rmsea.scaled cfi.scaled tli.scaled   srmr
meq.list.strong - meq.list.configural         4         0.04     -0.088     -0.115  0.028
meq.list.strict - meq.list.strong            10         0.07     -0.457     -0.300 -0.010

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   2.000   1.528   2.000   2.000 
group_1
  2   3   5 
213 217 214 
Iteration: 1, Log-Lik: -1615.292, Max-Change: 0.98733Iteration: 2, Log-Lik: -1537.285, Max-Change: 0.30911Iteration: 3, Log-Lik: -1524.121, Max-Change: 0.22081Iteration: 4, Log-Lik: -1520.128, Max-Change: 0.15584Iteration: 5, Log-Lik: -1518.675, Max-Change: 0.10896Iteration: 6, Log-Lik: -1518.069, Max-Change: 0.07741Iteration: 7, Log-Lik: -1517.572, Max-Change: 0.04107Iteration: 8, Log-Lik: -1517.514, Max-Change: 0.03402Iteration: 9, Log-Lik: -1517.478, Max-Change: 0.02855Iteration: 10, Log-Lik: -1517.416, Max-Change: 0.00902Iteration: 11, Log-Lik: -1517.415, Max-Change: 0.00781Iteration: 12, Log-Lik: -1517.414, Max-Change: 0.00708Iteration: 13, Log-Lik: -1517.411, Max-Change: 0.00329Iteration: 14, Log-Lik: -1517.411, Max-Change: 0.00262Iteration: 15, Log-Lik: -1517.411, Max-Change: 0.00191Iteration: 16, Log-Lik: -1517.411, Max-Change: 0.00132Iteration: 17, Log-Lik: -1517.411, Max-Change: 0.00119Iteration: 18, Log-Lik: -1517.411, Max-Change: 0.00107Iteration: 19, Log-Lik: -1517.410, Max-Change: 0.00040Iteration: 20, Log-Lik: -1517.410, Max-Change: 0.00038Iteration: 21, Log-Lik: -1517.410, Max-Change: 0.00037Iteration: 22, Log-Lik: -1517.410, Max-Change: 0.00027Iteration: 23, Log-Lik: -1517.410, Max-Change: 0.00028Iteration: 24, Log-Lik: -1517.410, Max-Change: 0.00025Iteration: 25, Log-Lik: -1517.410, Max-Change: 0.00024Iteration: 26, Log-Lik: -1517.410, Max-Change: 0.00022Iteration: 27, Log-Lik: -1517.410, Max-Change: 0.00021Iteration: 28, Log-Lik: -1517.410, Max-Change: 0.00019Iteration: 29, Log-Lik: -1517.410, Max-Change: 0.00019Iteration: 30, Log-Lik: -1517.410, Max-Change: 0.00017Iteration: 31, Log-Lik: -1517.410, Max-Change: 0.00017Iteration: 32, Log-Lik: -1517.410, Max-Change: 0.00015Iteration: 33, Log-Lik: -1517.410, Max-Change: 0.00015Iteration: 34, Log-Lik: -1517.410, Max-Change: 0.00013Iteration: 35, Log-Lik: -1517.410, Max-Change: 0.00013Iteration: 36, Log-Lik: -1517.410, Max-Change: 0.00012Iteration: 37, Log-Lik: -1517.410, Max-Change: 0.00012Iteration: 38, Log-Lik: -1517.410, Max-Change: 0.00011Iteration: 39, Log-Lik: -1517.410, Max-Change: 0.00010Iteration: 40, Log-Lik: -1517.410, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_1, model = 1, group = group_1)

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 40 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -1517.41
Estimated parameters: 24 
AIC = 3082.82
BIC = 3190.045; SABIC = 3113.846
G2 (21) = 33.02, p = 0.046
RMSEA = 0.03, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.738 0.544
Obedience    0.639 0.408
Curiosity    0.808 0.653
Considerate  0.591 0.349

SS loadings:  1.954 
Proportion Var:  0.489 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.599 0.359
Obedience    0.690 0.475
Curiosity    0.567 0.321
Considerate  0.447 0.200

SS loadings:  1.355 
Proportion Var:  0.339 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.561 0.315
Obedience    0.391 0.153
Curiosity    0.773 0.597
Considerate  0.733 0.537

SS loadings:  1.603 
Proportion Var:  0.401 

Factor correlations: 

   F1
F1  1
            M2 df          p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl
stats 14.89916  6 0.02105567 0.04802789 0.01729447 0.07926942 0.01806867    0.07523583 0.04622534
            TLI       CFI
stats 0.8869174 0.9623058
Iteration: 1, Log-Lik: -1615.292, Max-Change: 1.01120Iteration: 2, Log-Lik: -1539.508, Max-Change: 0.26770Iteration: 3, Log-Lik: -1527.606, Max-Change: 0.13344Iteration: 4, Log-Lik: -1524.320, Max-Change: 0.09682Iteration: 5, Log-Lik: -1523.247, Max-Change: 0.06755Iteration: 6, Log-Lik: -1522.860, Max-Change: 0.04707Iteration: 7, Log-Lik: -1522.634, Max-Change: 0.02464Iteration: 8, Log-Lik: -1522.601, Max-Change: 0.01801Iteration: 9, Log-Lik: -1522.582, Max-Change: 0.01378Iteration: 10, Log-Lik: -1522.559, Max-Change: 0.00421Iteration: 11, Log-Lik: -1522.558, Max-Change: 0.00347Iteration: 12, Log-Lik: -1522.557, Max-Change: 0.00295Iteration: 13, Log-Lik: -1522.556, Max-Change: 0.00089Iteration: 14, Log-Lik: -1522.556, Max-Change: 0.00023Iteration: 15, Log-Lik: -1522.556, Max-Change: 0.00020Iteration: 16, Log-Lik: -1522.556, Max-Change: 0.00065Iteration: 17, Log-Lik: -1522.556, Max-Change: 0.00044Iteration: 18, Log-Lik: -1522.556, Max-Change: 0.00030Iteration: 19, Log-Lik: -1522.556, Max-Change: 0.00022Iteration: 20, Log-Lik: -1522.556, Max-Change: 0.00003

Call:
multipleGroup(data = irt_data_1, model = 1, group = group_1, 
    invariance = c("slopes"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 20 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -1522.556
Estimated parameters: 24 
AIC = 3077.112
BIC = 3148.595; SABIC = 3097.796
G2 (29) = 43.31, p = 0.0426
RMSEA = 0.028, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.666 0.444
Obedience    0.596 0.355
Curiosity    0.723 0.522
Considerate  0.558 0.312

SS loadings:  1.632 
Proportion Var:  0.408 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.666 0.444
Obedience    0.596 0.355
Curiosity    0.723 0.522
Considerate  0.558 0.312

SS loadings:  1.632 
Proportion Var:  0.408 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.666 0.444
Obedience    0.596 0.355
Curiosity    0.723 0.522
Considerate  0.558 0.312

SS loadings:  1.632 
Proportion Var:  0.408 

Factor correlations: 

   F1
F1  1
            M2 df          p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl
stats 26.06012 14 0.02543509 0.03660213 0.01262119 0.05813592 0.05474527    0.09375132 0.06780139
            TLI       CFI
stats 0.9343218 0.9489169
Iteration: 1, Log-Lik: -1615.292, Max-Change: 0.43846Iteration: 2, Log-Lik: -1590.260, Max-Change: 0.34415Iteration: 3, Log-Lik: -1579.669, Max-Change: 0.24541Iteration: 4, Log-Lik: -1575.583, Max-Change: 0.18759Iteration: 5, Log-Lik: -1573.939, Max-Change: 0.15931Iteration: 6, Log-Lik: -1573.195, Max-Change: 0.13586Iteration: 7, Log-Lik: -1572.395, Max-Change: 0.09777Iteration: 8, Log-Lik: -1572.242, Max-Change: 0.09333Iteration: 9, Log-Lik: -1572.110, Max-Change: 0.08964Iteration: 10, Log-Lik: -1571.572, Max-Change: 0.07398Iteration: 11, Log-Lik: -1571.524, Max-Change: 0.06858Iteration: 12, Log-Lik: -1571.484, Max-Change: 0.06360Iteration: 13, Log-Lik: -1571.304, Max-Change: 0.03904Iteration: 14, Log-Lik: -1571.286, Max-Change: 0.03887Iteration: 15, Log-Lik: -1571.270, Max-Change: 0.03671Iteration: 16, Log-Lik: -1571.194, Max-Change: 0.03055Iteration: 17, Log-Lik: -1571.185, Max-Change: 0.03987Iteration: 18, Log-Lik: -1571.176, Max-Change: 0.03132Iteration: 19, Log-Lik: -1571.144, Max-Change: 0.02978Iteration: 20, Log-Lik: -1571.135, Max-Change: 0.02737Iteration: 21, Log-Lik: -1571.129, Max-Change: 0.02299Iteration: 22, Log-Lik: -1571.105, Max-Change: 0.02268Iteration: 23, Log-Lik: -1571.100, Max-Change: 0.02097Iteration: 24, Log-Lik: -1571.096, Max-Change: 0.02082Iteration: 25, Log-Lik: -1571.073, Max-Change: 0.02094Iteration: 26, Log-Lik: -1571.070, Max-Change: 0.01978Iteration: 27, Log-Lik: -1571.067, Max-Change: 0.01937Iteration: 28, Log-Lik: -1571.049, Max-Change: 0.01955Iteration: 29, Log-Lik: -1571.046, Max-Change: 0.01890Iteration: 30, Log-Lik: -1571.043, Max-Change: 0.01962Iteration: 31, Log-Lik: -1571.029, Max-Change: 0.01512Iteration: 32, Log-Lik: -1571.026, Max-Change: 0.01824Iteration: 33, Log-Lik: -1571.024, Max-Change: 0.01744Iteration: 34, Log-Lik: -1571.015, Max-Change: 0.01763Iteration: 35, Log-Lik: -1571.013, Max-Change: 0.01914Iteration: 36, Log-Lik: -1571.011, Max-Change: 0.01812Iteration: 37, Log-Lik: -1571.002, Max-Change: 0.02177Iteration: 38, Log-Lik: -1570.998, Max-Change: 0.01679Iteration: 39, Log-Lik: -1570.997, Max-Change: 0.01793Iteration: 40, Log-Lik: -1570.988, Max-Change: 0.01789Iteration: 41, Log-Lik: -1570.986, Max-Change: 0.01348Iteration: 42, Log-Lik: -1570.985, Max-Change: 0.01353Iteration: 43, Log-Lik: -1570.979, Max-Change: 0.01423Iteration: 44, Log-Lik: -1570.978, Max-Change: 0.01440Iteration: 45, Log-Lik: -1570.977, Max-Change: 0.01663Iteration: 46, Log-Lik: -1570.972, Max-Change: 0.00461Iteration: 47, Log-Lik: -1570.971, Max-Change: 0.01043Iteration: 48, Log-Lik: -1570.970, Max-Change: 0.01296Iteration: 49, Log-Lik: -1570.967, Max-Change: 0.01359Iteration: 50, Log-Lik: -1570.966, Max-Change: 0.00960Iteration: 51, Log-Lik: -1570.966, Max-Change: 0.01376Iteration: 52, Log-Lik: -1570.964, Max-Change: 0.01208Iteration: 53, Log-Lik: -1570.963, Max-Change: 0.00354Iteration: 54, Log-Lik: -1570.963, Max-Change: 0.01024Iteration: 55, Log-Lik: -1570.962, 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Call:
multipleGroup(data = irt_data_1, model = 1, group = group_1, 
    invariance = c("intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 385 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -1570.947
Estimated parameters: 24 
AIC = 3173.893
BIC = 3245.377; SABIC = 3194.577
G2 (29) = 140.09, p = 0
RMSEA = 0.077, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.597 0.356
Obedience    0.950 0.903
Curiosity    0.657 0.432
Considerate  0.560 0.314

SS loadings:  2.005 
Proportion Var:  0.501 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.426 0.182
Obedience    0.953 0.908
Curiosity    0.349 0.122
Considerate  0.467 0.218

SS loadings:  1.43 
Proportion Var:  0.358 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.761 0.579
Obedience    0.426 0.181
Curiosity    0.764 0.584
Considerate  0.675 0.456

SS loadings:  1.8 
Proportion Var:  0.45 

Factor correlations: 

   F1
F1  1
           M2 df p     RMSEA    RMSEA_5  RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl       TLI
stats 118.988 14 0 0.1079942 0.09049697 0.1262096 0.08746885     0.0909596 0.07861256 0.4282457
            CFI
stats 0.5553022
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_metricb_1    3173.893 3194.577 3201.632 3245.377 -1570.947             
mod_configural_1 3082.820 3113.846 3124.428 3190.045 -1517.410 107.073  8 0
                      AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_metric_1     3077.112 3097.796 3104.850 3148.595 -1522.556                
mod_configural_1 3082.820 3113.846 3124.428 3190.045 -1517.410 10.292  8 0.245
Iteration: 1, Log-Lik: -1615.292, Max-Change: 0.34418Iteration: 2, Log-Lik: -1569.253, Max-Change: 0.23349Iteration: 3, Log-Lik: -1551.829, Max-Change: 0.15693Iteration: 4, Log-Lik: -1546.292, Max-Change: 0.10618Iteration: 5, Log-Lik: -1543.772, Max-Change: 0.07729Iteration: 6, Log-Lik: -1542.148, Max-Change: 0.05999Iteration: 7, Log-Lik: -1540.368, Max-Change: 0.06452Iteration: 8, Log-Lik: -1538.880, Max-Change: 0.03188Iteration: 9, Log-Lik: -1538.258, Max-Change: 0.02852Iteration: 10, Log-Lik: -1537.609, Max-Change: 0.04391Iteration: 11, Log-Lik: -1536.883, Max-Change: 0.02202Iteration: 12, Log-Lik: -1536.596, Max-Change: 0.01873Iteration: 13, Log-Lik: -1536.319, Max-Change: 0.02947Iteration: 14, Log-Lik: -1535.947, Max-Change: 0.01598Iteration: 15, Log-Lik: -1535.804, Max-Change: 0.01362Iteration: 16, Log-Lik: -1535.656, Max-Change: 0.01380Iteration: 17, Log-Lik: -1535.534, Max-Change: 0.01120Iteration: 18, Log-Lik: -1535.457, Max-Change: 0.00981Iteration: 19, Log-Lik: -1535.400, Max-Change: 0.01484Iteration: 20, Log-Lik: -1535.278, Max-Change: 0.00902Iteration: 21, Log-Lik: -1535.233, Max-Change: 0.00768Iteration: 22, Log-Lik: -1535.193, Max-Change: 0.00694Iteration: 23, Log-Lik: -1535.162, Max-Change: 0.00614Iteration: 24, Log-Lik: -1535.136, Max-Change: 0.00553Iteration: 25, Log-Lik: -1535.116, Max-Change: 0.00676Iteration: 26, Log-Lik: -1535.077, Max-Change: 0.00540Iteration: 27, Log-Lik: -1535.058, Max-Change: 0.00461Iteration: 28, Log-Lik: -1535.043, Max-Change: 0.00416Iteration: 29, Log-Lik: -1535.031, Max-Change: 0.00373Iteration: 30, Log-Lik: -1535.020, Max-Change: 0.00379Iteration: 31, Log-Lik: -1535.011, Max-Change: 0.00435Iteration: 32, Log-Lik: -1534.995, Max-Change: 0.00347Iteration: 33, Log-Lik: -1534.986, Max-Change: 0.00307Iteration: 34, Log-Lik: -1534.979, Max-Change: 0.00283Iteration: 35, Log-Lik: -1534.973, Max-Change: 0.00260Iteration: 36, Log-Lik: -1534.968, Max-Change: 0.00271Iteration: 37, Log-Lik: -1534.963, Max-Change: 0.00295Iteration: 38, Log-Lik: -1534.955, Max-Change: 0.00251Iteration: 39, Log-Lik: -1534.951, Max-Change: 0.00225Iteration: 40, Log-Lik: -1534.947, Max-Change: 0.00207Iteration: 41, Log-Lik: -1534.944, Max-Change: 0.00191Iteration: 42, Log-Lik: -1534.941, Max-Change: 0.00198Iteration: 43, Log-Lik: -1534.938, Max-Change: 0.00209Iteration: 44, Log-Lik: -1534.934, Max-Change: 0.00186Iteration: 45, Log-Lik: -1534.932, Max-Change: 0.00167Iteration: 46, Log-Lik: -1534.930, Max-Change: 0.00154Iteration: 47, Log-Lik: -1534.928, Max-Change: 0.00143Iteration: 48, Log-Lik: -1534.926, Max-Change: 0.00147Iteration: 49, Log-Lik: -1534.925, Max-Change: 0.00155Iteration: 50, Log-Lik: -1534.922, Max-Change: 0.00139Iteration: 51, Log-Lik: -1534.921, Max-Change: 0.00126Iteration: 52, Log-Lik: -1534.919, Max-Change: 0.00117Iteration: 53, Log-Lik: -1534.918, Max-Change: 0.00108Iteration: 54, Log-Lik: -1534.917, Max-Change: 0.00112Iteration: 55, Log-Lik: -1534.916, Max-Change: 0.00131Iteration: 56, Log-Lik: -1534.915, Max-Change: 0.00106Iteration: 57, Log-Lik: -1534.914, Max-Change: 0.00096Iteration: 58, Log-Lik: -1534.913, Max-Change: 0.00089Iteration: 59, Log-Lik: -1534.913, Max-Change: 0.00083Iteration: 60, Log-Lik: -1534.912, Max-Change: 0.00083Iteration: 61, Log-Lik: -1534.912, Max-Change: 0.00107Iteration: 62, Log-Lik: -1534.911, Max-Change: 0.00081Iteration: 63, Log-Lik: -1534.910, Max-Change: 0.00074Iteration: 64, Log-Lik: -1534.910, Max-Change: 0.00068Iteration: 65, Log-Lik: -1534.909, Max-Change: 0.00064Iteration: 66, Log-Lik: -1534.909, Max-Change: 0.00064Iteration: 67, Log-Lik: -1534.909, Max-Change: 0.00086Iteration: 68, Log-Lik: -1534.908, Max-Change: 0.00062Iteration: 69, Log-Lik: -1534.908, Max-Change: 0.00057Iteration: 70, Log-Lik: -1534.907, Max-Change: 0.00052Iteration: 71, Log-Lik: -1534.907, Max-Change: 0.00049Iteration: 72, Log-Lik: -1534.907, Max-Change: 0.00050Iteration: 73, Log-Lik: -1534.907, Max-Change: 0.00068Iteration: 74, Log-Lik: -1534.906, Max-Change: 0.00048Iteration: 75, Log-Lik: -1534.906, Max-Change: 0.00044Iteration: 76, Log-Lik: -1534.906, Max-Change: 0.00041Iteration: 77, Log-Lik: -1534.906, Max-Change: 0.00039Iteration: 78, Log-Lik: -1534.906, Max-Change: 0.00039Iteration: 79, Log-Lik: -1534.906, Max-Change: 0.00054Iteration: 80, Log-Lik: -1534.906, Max-Change: 0.00037Iteration: 81, Log-Lik: -1534.905, Max-Change: 0.00034Iteration: 82, Log-Lik: -1534.905, Max-Change: 0.00031Iteration: 83, Log-Lik: -1534.905, Max-Change: 0.00029Iteration: 84, Log-Lik: -1534.905, Max-Change: 0.00030Iteration: 85, Log-Lik: -1534.905, Max-Change: 0.00043Iteration: 86, Log-Lik: -1534.905, Max-Change: 0.00029Iteration: 87, Log-Lik: -1534.905, Max-Change: 0.00026Iteration: 88, Log-Lik: -1534.905, Max-Change: 0.00025Iteration: 89, Log-Lik: -1534.905, Max-Change: 0.00023Iteration: 90, Log-Lik: -1534.905, Max-Change: 0.00023Iteration: 91, Log-Lik: -1534.905, Max-Change: 0.00033Iteration: 92, Log-Lik: -1534.905, Max-Change: 0.00022Iteration: 93, Log-Lik: -1534.905, Max-Change: 0.00021Iteration: 94, Log-Lik: -1534.905, Max-Change: 0.00019Iteration: 95, Log-Lik: -1534.904, Max-Change: 0.00018Iteration: 96, Log-Lik: -1534.904, Max-Change: 0.00017Iteration: 97, Log-Lik: -1534.904, Max-Change: 0.00026Iteration: 98, Log-Lik: -1534.904, Max-Change: 0.00017Iteration: 99, Log-Lik: -1534.904, Max-Change: 0.00016Iteration: 100, Log-Lik: -1534.904, Max-Change: 0.00015Iteration: 101, Log-Lik: -1534.904, Max-Change: 0.00014Iteration: 102, Log-Lik: -1534.904, Max-Change: 0.00014Iteration: 103, Log-Lik: -1534.904, Max-Change: 0.00021Iteration: 104, Log-Lik: -1534.904, Max-Change: 0.00013Iteration: 105, Log-Lik: -1534.904, Max-Change: 0.00012Iteration: 106, Log-Lik: -1534.904, Max-Change: 0.00011Iteration: 107, Log-Lik: -1534.904, Max-Change: 0.00011Iteration: 108, Log-Lik: -1534.904, Max-Change: 0.00012Iteration: 109, Log-Lik: -1534.904, Max-Change: 0.00015Iteration: 110, Log-Lik: -1534.904, Max-Change: 0.00010Iteration: 111, Log-Lik: -1534.904, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_1, model = 1, group = group_1, 
    invariance = c("slopes", "intercepts", "free_var", "free_means"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 111 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -1534.904
Estimated parameters: 28 
AIC = 3093.808
BIC = 3147.421; SABIC = 3109.321
G2 (33) = 68.01, p = 3e-04
RMSEA = 0.041, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.803 0.645
Obedience    0.743 0.552
Curiosity    0.677 0.458
Considerate  0.541 0.293

SS loadings:  1.949 
Proportion Var:  0.487 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.685 0.469
Obedience    0.612 0.375
Curiosity    0.540 0.291
Considerate  0.410 0.168

SS loadings:  1.303 
Proportion Var:  0.326 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.692 0.478
Obedience    0.619 0.383
Curiosity    0.547 0.299
Considerate  0.416 0.173

SS loadings:  1.333 
Proportion Var:  0.333 

Factor correlations: 

   F1
F1  1
            M2 df            p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy. SRMSR.Control
stats 50.93772 18 5.439145e-05 0.05334639 0.03637975 0.07084127 0.05558265    0.07832952
      SRMSR.Girl       TLI       CFI
stats 0.09992184 0.8604857 0.8604857
                      AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_scalar2_1    3093.808 3109.321 3114.612 3147.421 -1534.904            
mod_configural_1 3082.820 3113.846 3124.428 3190.045 -1517.410 34.988 12 0
Iteration: 1, Log-Lik: -1615.292, Max-Change: 0.27492Iteration: 2, Log-Lik: -1594.924, Max-Change: 0.19453Iteration: 3, Log-Lik: -1588.029, Max-Change: 0.12598Iteration: 4, Log-Lik: -1585.829, Max-Change: 0.08427Iteration: 5, Log-Lik: -1584.887, Max-Change: 0.06056Iteration: 6, Log-Lik: -1584.319, Max-Change: 0.04656Iteration: 7, Log-Lik: -1583.719, Max-Change: 0.05291Iteration: 8, Log-Lik: -1583.364, Max-Change: 0.03970Iteration: 9, Log-Lik: -1583.110, Max-Change: 0.03261Iteration: 10, Log-Lik: -1582.858, Max-Change: 0.03536Iteration: 11, Log-Lik: -1582.625, Max-Change: 0.02654Iteration: 12, Log-Lik: -1582.465, Max-Change: 0.02205Iteration: 13, Log-Lik: -1582.303, Max-Change: 0.02398Iteration: 14, Log-Lik: -1582.162, Max-Change: 0.01865Iteration: 15, Log-Lik: -1582.057, Max-Change: 0.01655Iteration: 16, Log-Lik: -1581.950, Max-Change: 0.02325Iteration: 17, Log-Lik: -1581.838, Max-Change: 0.01714Iteration: 18, Log-Lik: -1581.764, Max-Change: 0.01470Iteration: 19, Log-Lik: -1581.690, Max-Change: 0.01672Iteration: 20, Log-Lik: -1581.628, Max-Change: 0.01365Iteration: 21, Log-Lik: -1581.578, Max-Change: 0.01223Iteration: 22, Log-Lik: -1581.537, Max-Change: 0.01952Iteration: 23, Log-Lik: -1581.461, Max-Change: 0.01341Iteration: 24, Log-Lik: -1581.422, Max-Change: 0.01093Iteration: 25, Log-Lik: -1581.389, Max-Change: 0.01022Iteration: 26, Log-Lik: -1581.362, Max-Change: 0.00908Iteration: 27, Log-Lik: -1581.338, Max-Change: 0.00840Iteration: 28, Log-Lik: -1581.319, Max-Change: 0.01385Iteration: 29, Log-Lik: -1581.279, Max-Change: 0.00954Iteration: 30, Log-Lik: -1581.259, Max-Change: 0.00772Iteration: 31, Log-Lik: -1581.243, Max-Change: 0.00701Iteration: 32, Log-Lik: -1581.230, Max-Change: 0.00629Iteration: 33, Log-Lik: -1581.218, Max-Change: 0.00584Iteration: 34, Log-Lik: -1581.208, Max-Change: 0.00954Iteration: 35, Log-Lik: -1581.188, Max-Change: 0.00663Iteration: 36, Log-Lik: -1581.178, Max-Change: 0.00538Iteration: 37, Log-Lik: -1581.170, Max-Change: 0.00486Iteration: 38, Log-Lik: -1581.164, Max-Change: 0.00438Iteration: 39, Log-Lik: -1581.158, Max-Change: 0.00406Iteration: 40, Log-Lik: -1581.153, Max-Change: 0.00659Iteration: 41, Log-Lik: -1581.143, Max-Change: 0.00461Iteration: 42, Log-Lik: -1581.138, Max-Change: 0.00375Iteration: 43, Log-Lik: -1581.134, Max-Change: 0.00338Iteration: 44, Log-Lik: -1581.131, Max-Change: 0.00305Iteration: 45, Log-Lik: -1581.128, Max-Change: 0.00284Iteration: 46, Log-Lik: -1581.125, Max-Change: 0.00456Iteration: 47, Log-Lik: -1581.120, Max-Change: 0.00322Iteration: 48, Log-Lik: -1581.118, Max-Change: 0.00262Iteration: 49, Log-Lik: -1581.116, Max-Change: 0.00236Iteration: 50, Log-Lik: -1581.114, Max-Change: 0.00213Iteration: 51, Log-Lik: -1581.113, Max-Change: 0.00198Iteration: 52, Log-Lik: -1581.112, Max-Change: 0.00318Iteration: 53, Log-Lik: -1581.109, Max-Change: 0.00225Iteration: 54, Log-Lik: -1581.108, Max-Change: 0.00184Iteration: 55, Log-Lik: -1581.107, Max-Change: 0.00166Iteration: 56, Log-Lik: -1581.106, Max-Change: 0.00150Iteration: 57, Log-Lik: -1581.105, Max-Change: 0.00139Iteration: 58, Log-Lik: -1581.105, Max-Change: 0.00224Iteration: 59, Log-Lik: -1581.103, Max-Change: 0.00159Iteration: 60, Log-Lik: -1581.103, Max-Change: 0.00129Iteration: 61, Log-Lik: -1581.102, Max-Change: 0.00116Iteration: 62, Log-Lik: -1581.102, Max-Change: 0.00105Iteration: 63, Log-Lik: -1581.102, Max-Change: 0.00097Iteration: 64, Log-Lik: -1581.101, Max-Change: 0.00156Iteration: 65, Log-Lik: -1581.101, Max-Change: 0.00111Iteration: 66, Log-Lik: -1581.100, Max-Change: 0.00091Iteration: 67, Log-Lik: -1581.100, Max-Change: 0.00082Iteration: 68, Log-Lik: -1581.100, Max-Change: 0.00074Iteration: 69, Log-Lik: -1581.100, Max-Change: 0.00069Iteration: 70, Log-Lik: -1581.100, Max-Change: 0.00110Iteration: 71, Log-Lik: -1581.099, Max-Change: 0.00078Iteration: 72, Log-Lik: -1581.099, Max-Change: 0.00064Iteration: 73, Log-Lik: -1581.099, Max-Change: 0.00058Iteration: 74, Log-Lik: -1581.099, Max-Change: 0.00052Iteration: 75, Log-Lik: -1581.099, Max-Change: 0.00049Iteration: 76, Log-Lik: -1581.099, Max-Change: 0.00078Iteration: 77, Log-Lik: -1581.099, Max-Change: 0.00055Iteration: 78, Log-Lik: -1581.098, Max-Change: 0.00045Iteration: 79, Log-Lik: -1581.098, Max-Change: 0.00041Iteration: 80, Log-Lik: -1581.098, Max-Change: 0.00037Iteration: 81, Log-Lik: -1581.098, Max-Change: 0.00034Iteration: 82, Log-Lik: -1581.098, Max-Change: 0.00056Iteration: 83, Log-Lik: -1581.098, Max-Change: 0.00039Iteration: 84, Log-Lik: -1581.098, Max-Change: 0.00032Iteration: 85, Log-Lik: -1581.098, Max-Change: 0.00029Iteration: 86, Log-Lik: -1581.098, Max-Change: 0.00026Iteration: 87, Log-Lik: -1581.098, Max-Change: 0.00024Iteration: 88, Log-Lik: -1581.098, Max-Change: 0.00039Iteration: 89, Log-Lik: -1581.098, Max-Change: 0.00027Iteration: 90, Log-Lik: -1581.098, Max-Change: 0.00022Iteration: 91, Log-Lik: -1581.098, Max-Change: 0.00020Iteration: 92, Log-Lik: -1581.098, Max-Change: 0.00018Iteration: 93, Log-Lik: -1581.098, Max-Change: 0.00017Iteration: 94, Log-Lik: -1581.098, Max-Change: 0.00027Iteration: 95, Log-Lik: -1581.098, Max-Change: 0.00020Iteration: 96, Log-Lik: -1581.098, Max-Change: 0.00016Iteration: 97, Log-Lik: -1581.098, Max-Change: 0.00014Iteration: 98, Log-Lik: -1581.098, Max-Change: 0.00013Iteration: 99, Log-Lik: -1581.098, Max-Change: 0.00012Iteration: 100, Log-Lik: -1581.098, Max-Change: 0.00020Iteration: 101, Log-Lik: -1581.098, Max-Change: 0.00014Iteration: 102, Log-Lik: -1581.098, Max-Change: 0.00011Iteration: 103, Log-Lik: -1581.098, Max-Change: 0.00010Iteration: 104, Log-Lik: -1581.098, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_1, model = 1, group = group_1, 
    invariance = c("slopes", "intercepts", "free_var"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 104 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -1581.098
Estimated parameters: 26 
AIC = 3182.196
BIC = 3226.873; SABIC = 3195.123
G2 (35) = 160.39, p = 0
RMSEA = 0.075, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.778 0.605
Obedience    0.721 0.519
Curiosity    0.768 0.590
Considerate  0.626 0.392

SS loadings:  2.107 
Proportion Var:  0.527 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.651 0.424
Obedience    0.585 0.342
Curiosity    0.640 0.410
Considerate  0.487 0.237

SS loadings:  1.412 
Proportion Var:  0.353 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.729 0.532
Obedience    0.667 0.445
Curiosity    0.719 0.517
Considerate  0.569 0.324

SS loadings:  1.817 
Proportion Var:  0.454 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5  RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl     TLI
stats 135.3371 20 0 0.09470306 0.07988047 0.1100485 0.04169288    0.08371039  0.1160275 0.56032
            CFI
stats 0.5114667
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar1_1    3182.196 3195.123 3199.532 3226.873 -1581.098             
mod_configural_1 3082.820 3113.846 3124.428 3190.045 -1517.410 127.375 14 0
Iteration: 1, Log-Lik: -1615.292, Max-Change: 0.27492Iteration: 2, Log-Lik: -1596.214, Max-Change: 0.21057Iteration: 3, Log-Lik: -1588.427, Max-Change: 0.14675Iteration: 4, Log-Lik: -1585.571, Max-Change: 0.09920Iteration: 5, Log-Lik: -1584.532, Max-Change: 0.06688Iteration: 6, Log-Lik: -1584.143, Max-Change: 0.04525Iteration: 7, Log-Lik: -1583.927, Max-Change: 0.02166Iteration: 8, Log-Lik: -1583.901, Max-Change: 0.01491Iteration: 9, Log-Lik: -1583.889, Max-Change: 0.01084Iteration: 10, Log-Lik: -1583.878, Max-Change: 0.00406Iteration: 11, Log-Lik: -1583.877, Max-Change: 0.00305Iteration: 12, Log-Lik: -1583.876, Max-Change: 0.00245Iteration: 13, Log-Lik: -1583.876, Max-Change: 0.00090Iteration: 14, Log-Lik: -1583.876, Max-Change: 0.00014Iteration: 15, Log-Lik: -1583.876, Max-Change: 0.00013Iteration: 16, Log-Lik: -1583.876, Max-Change: 0.00048Iteration: 17, Log-Lik: -1583.876, Max-Change: 0.00005

Call:
multipleGroup(data = irt_data_1, model = 1, group = group_1, 
    invariance = c("slopes", "intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 17 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -1583.876
Estimated parameters: 24 
AIC = 3183.752
BIC = 3219.493; SABIC = 3194.094
G2 (37) = 165.95, p = 0
RMSEA = 0.074, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.727 0.529
Obedience    0.672 0.452
Curiosity    0.703 0.494
Considerate  0.559 0.312

SS loadings:  1.787 
Proportion Var:  0.447 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.727 0.529
Obedience    0.672 0.452
Curiosity    0.703 0.494
Considerate  0.559 0.312

SS loadings:  1.787 
Proportion Var:  0.447 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.727 0.529
Obedience    0.672 0.452
Curiosity    0.703 0.494
Considerate  0.559 0.312

SS loadings:  1.787 
Proportion Var:  0.447 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5  RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl       TLI
stats 140.0758 22 0 0.09136157 0.07717471 0.1060265 0.04366268     0.1115154  0.1147203 0.5907998
            CFI
stats 0.4998664
                         AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_fullconstrain_1 3183.752 3194.094 3197.621 3219.493 -1583.876             
mod_configural_1    3082.820 3113.846 3124.428 3190.045 -1517.410 132.931 16 0
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_metricb_1    3173.893 3194.577 3201.632 3245.377 -1570.947             
mod_configural_1 3082.820 3113.846 3124.428 3190.045 -1517.410 107.073  8 0
                   AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_scalar2_1 3093.808 3109.321 3114.612 3147.421 -1534.904            
mod_metric_1  3077.112 3097.796 3104.850 3148.595 -1522.556 24.696  4 0
                   AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_scalar1_1 3182.196 3195.123 3199.532 3226.873 -1581.098            
mod_scalar2_1 3093.808 3109.321 3114.612 3147.421 -1534.904 92.387  2 0
                         AIC    SABIC       HQ      BIC    logLik    X2 df     p
mod_fullconstrain_1 3183.752 3194.094 3197.621 3219.493 -1583.876               
mod_scalar1_1       3182.196 3195.123 3199.532 3226.873 -1581.098 5.556  2 0.062
                         AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_fullconstrain_1 3183.752 3194.094 3197.621 3219.493 -1583.876                
mod_scalar2_1       3093.808 3109.321 3114.612 3147.421 -1534.904 97.943  4     0
mod_metric_1        3077.112 3097.796 3104.850 3148.595 -1522.556 24.696  4     0
mod_configural_1    3082.820 3113.846 3124.428 3190.045 -1517.410 10.292  8 0.245
                         AIC    SABIC       HQ      BIC    logLik      X2 df   p
mod_fullconstrain_1 3183.752 3194.094 3197.621 3219.493 -1583.876               
mod_scalar2_1       3093.808 3109.321 3114.612 3147.421 -1534.904  97.943  4   0
mod_metricb_1       3173.893 3194.577 3201.632 3245.377 -1570.947 -72.085  4 NaN
mod_configural_1    3082.820 3113.846 3124.428 3190.045 -1517.410 107.073  8   0
################### Nested Model Comparison #########################

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

lavaan->unknown():  
   lavaan NOTE: The “Chisq” column contains standard test statistics, not the robust test that 
   should be reported per model. A robust difference test is a function of two standard (not 
   robust) statistics.
                    Df AIC BIC   Chisq Chisq diff Df diff Pr(>Chisq)    
meq.list.configural  6          15.586                                  
meq.list.strong     10          85.164     81.432       4  < 2.2e-16 ***
meq.list.means      12         304.794    165.991       2  < 2.2e-16 ***
meq.list.strict     20         450.334    152.736       8  < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

####################### Model Fit Indices ###########################
                    chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled tli.scaled  srmr
meq.list.configural      21.737†         6          .001        .068†      .981†      .942† .049†
meq.list.strong         108.982         10          .000        .133       .878       .781  .082 
meq.list.means          340.140         12          .000        .221       .597       .395  .077 
meq.list.strict         474.351         20          .000        .201       .442       .498  .062 

################## Differences in Fit Indices #######################
                                      df.scaled rmsea.scaled cfi.scaled tli.scaled   srmr
meq.list.strong - meq.list.configural         4        0.064     -0.102     -0.161  0.034
meq.list.means - meq.list.strong              2        0.088     -0.282     -0.386 -0.005
meq.list.strict - meq.list.means              8       -0.020     -0.155      0.102 -0.015

group_2
  1   2   3 
560 562 562 
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Call:
multipleGroup(data = irt_data_2, model = 1, group = group_2)

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 277 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -3930.205
Estimated parameters: 24 
AIC = 7908.41
BIC = 8038.704; SABIC = 7962.459
G2 (21) = 36.54, p = 0.019
RMSEA = 0.021, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.510 0.260
Obedience    0.539 0.290
Curiosity    0.929 0.864
Considerate  0.514 0.264

SS loadings:  1.677 
Proportion Var:  0.419 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.720 0.518
Obedience    0.712 0.507
Curiosity    0.772 0.596
Considerate  0.491 0.242

SS loadings:  1.862 
Proportion Var:  0.466 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.640 0.410
Obedience    0.497 0.247
Curiosity    0.842 0.709
Considerate  0.563 0.317

SS loadings:  1.682 
Proportion Var:  0.42 

Factor correlations: 

   F1
F1  1
            M2 df            p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy. SRMSR.Control
stats 23.72625  6 0.0005863942 0.04189776 0.02507907 0.06022228  0.0329102    0.03862921
      SRMSR.Girl       TLI       CFI
stats  0.0403479 0.9186552 0.9728851
Iteration: 1, Log-Lik: -4196.320, Max-Change: 1.04890Iteration: 2, Log-Lik: -3989.101, Max-Change: 0.27270Iteration: 3, Log-Lik: -3955.338, Max-Change: 0.21272Iteration: 4, Log-Lik: -3944.435, Max-Change: 0.16082Iteration: 5, Log-Lik: -3940.340, Max-Change: 0.12384Iteration: 6, Log-Lik: -3938.539, Max-Change: 0.09693Iteration: 7, Log-Lik: -3936.757, Max-Change: 0.04844Iteration: 8, Log-Lik: -3936.539, Max-Change: 0.04282Iteration: 9, Log-Lik: -3936.377, Max-Change: 0.03792Iteration: 10, Log-Lik: -3935.882, Max-Change: 0.01549Iteration: 11, Log-Lik: -3935.866, Max-Change: 0.01193Iteration: 12, Log-Lik: -3935.855, Max-Change: 0.01108Iteration: 13, Log-Lik: -3935.813, Max-Change: 0.00563Iteration: 14, Log-Lik: -3935.810, Max-Change: 0.00507Iteration: 15, Log-Lik: -3935.808, Max-Change: 0.00464Iteration: 16, Log-Lik: -3935.801, Max-Change: 0.00243Iteration: 17, Log-Lik: -3935.800, Max-Change: 0.00221Iteration: 18, Log-Lik: -3935.800, Max-Change: 0.00184Iteration: 19, Log-Lik: -3935.800, Max-Change: 0.00157Iteration: 20, Log-Lik: -3935.799, Max-Change: 0.00150Iteration: 21, Log-Lik: -3935.799, Max-Change: 0.00138Iteration: 22, Log-Lik: -3935.798, Max-Change: 0.00037Iteration: 23, Log-Lik: -3935.798, Max-Change: 0.00036Iteration: 24, Log-Lik: -3935.798, Max-Change: 0.00175Iteration: 25, Log-Lik: -3935.798, Max-Change: 0.00145Iteration: 26, Log-Lik: -3935.798, Max-Change: 0.00024Iteration: 27, Log-Lik: -3935.798, Max-Change: 0.00023Iteration: 28, Log-Lik: -3935.798, Max-Change: 0.00098Iteration: 29, Log-Lik: -3935.798, Max-Change: 0.00018Iteration: 30, Log-Lik: -3935.798, Max-Change: 0.00017Iteration: 31, Log-Lik: -3935.798, Max-Change: 0.00073Iteration: 32, Log-Lik: -3935.798, Max-Change: 0.00013Iteration: 33, Log-Lik: -3935.798, Max-Change: 0.00012Iteration: 34, Log-Lik: -3935.798, Max-Change: 0.00057Iteration: 35, Log-Lik: -3935.798, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_2, model = 1, group = group_2, 
    invariance = c("slopes"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 35 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -3935.798
Estimated parameters: 24 
AIC = 7903.596
BIC = 7990.459; SABIC = 7939.629
G2 (29) = 47.73, p = 0.0157
RMSEA = 0.02, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.621 0.386
Obedience    0.592 0.350
Curiosity    0.845 0.713
Considerate  0.525 0.276

SS loadings:  1.725 
Proportion Var:  0.431 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.621 0.386
Obedience    0.592 0.350
Curiosity    0.845 0.713
Considerate  0.525 0.276

SS loadings:  1.725 
Proportion Var:  0.431 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.621 0.386
Obedience    0.592 0.350
Curiosity    0.845 0.713
Considerate  0.525 0.276

SS loadings:  1.725 
Proportion Var:  0.431 

Factor correlations: 

   F1
F1  1
            M2 df           p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl
stats 35.36548 14 0.001297548 0.03011274 0.01788925 0.04263522 0.04580823    0.05691726 0.04169749
            TLI       CFI
stats 0.9579807 0.9673183
                      AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_metric_2     7903.596 7939.629 7935.768 7990.459 -3935.798                
mod_configural_2 7908.410 7962.459 7956.668 8038.704 -3930.205 11.186  8 0.191
Iteration: 1, Log-Lik: -4196.320, Max-Change: 0.53208Iteration: 2, Log-Lik: -4131.041, Max-Change: 0.44546Iteration: 3, Log-Lik: -4101.732, Max-Change: 0.39154Iteration: 4, Log-Lik: -4088.582, Max-Change: 0.29034Iteration: 5, Log-Lik: -4082.528, Max-Change: 0.23414Iteration: 6, Log-Lik: -4079.333, Max-Change: 0.21069Iteration: 7, Log-Lik: -4072.457, Max-Change: 0.26545Iteration: 8, Log-Lik: -4070.783, Max-Change: 0.28818Iteration: 9, Log-Lik: -4069.179, Max-Change: 0.33663Iteration: 10, Log-Lik: -4061.298, Max-Change: 0.77277Iteration: 11, Log-Lik: -4059.146, Max-Change: 0.95586Iteration: 12, Log-Lik: -4057.089, Max-Change: 1.02777Iteration: 13, Log-Lik: -4050.379, Max-Change: 1.56284Iteration: 14, Log-Lik: -4049.178, Max-Change: 1.91507Iteration: 15, Log-Lik: -4048.305, Max-Change: 1.96268Iteration: 16, Log-Lik: -4046.351, Max-Change: 0.22020Iteration: 17, Log-Lik: -4045.877, Max-Change: 0.34249Iteration: 18, Log-Lik: -4045.723, Max-Change: 2.18367Iteration: 19, 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0.00011Iteration: 234, Log-Lik: -4042.952, Max-Change: 0.00027Iteration: 235, Log-Lik: -4042.952, Max-Change: 0.00016Iteration: 236, Log-Lik: -4042.952, Max-Change: 0.00039Iteration: 237, Log-Lik: -4042.952, Max-Change: 0.00012Iteration: 238, Log-Lik: -4042.952, Max-Change: 0.00011Iteration: 239, Log-Lik: -4042.952, Max-Change: 0.00026Iteration: 240, Log-Lik: -4042.952, Max-Change: 0.00042Iteration: 241, Log-Lik: -4042.952, Max-Change: 0.00030Iteration: 242, Log-Lik: -4042.952, Max-Change: 0.00049Iteration: 243, Log-Lik: -4042.952, Max-Change: 0.00016Iteration: 244, Log-Lik: -4042.952, Max-Change: 0.00014Iteration: 245, Log-Lik: -4042.952, Max-Change: 0.00033Iteration: 246, Log-Lik: -4042.952, Max-Change: 0.00011Iteration: 247, Log-Lik: -4042.952, Max-Change: 0.00046Iteration: 248, Log-Lik: -4042.952, Max-Change: 0.00015Iteration: 249, Log-Lik: -4042.952, Max-Change: 0.00035Iteration: 250, Log-Lik: -4042.952, Max-Change: 0.00022Iteration: 251, Log-Lik: -4042.952, Max-Change: 0.00011Iteration: 252, Log-Lik: -4042.952, Max-Change: 0.00026Iteration: 253, Log-Lik: -4042.952, Max-Change: 0.00015Iteration: 254, Log-Lik: -4042.952, Max-Change: 0.00036Iteration: 255, Log-Lik: -4042.952, Max-Change: 0.00012Iteration: 256, Log-Lik: -4042.952, Max-Change: 0.00010Iteration: 257, Log-Lik: -4042.952, Max-Change: 0.00025Iteration: 258, Log-Lik: -4042.952, Max-Change: 0.00039Iteration: 259, Log-Lik: -4042.952, Max-Change: 0.00029Iteration: 260, Log-Lik: -4042.952, Max-Change: 0.00046Iteration: 261, Log-Lik: -4042.952, Max-Change: 0.00015Iteration: 262, Log-Lik: -4042.952, Max-Change: 0.00013Iteration: 263, Log-Lik: -4042.952, Max-Change: 0.00031Iteration: 264, Log-Lik: -4042.952, Max-Change: 0.00010

Call:
multipleGroup(data = irt_data_2, model = 1, group = group_2, 
    invariance = c("intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 264 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4042.952
Estimated parameters: 24 
AIC = 8117.903
BIC = 8204.766; SABIC = 8153.936
G2 (29) = 262.04, p = 0
RMSEA = 0.069, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.475 0.226
Obedience    0.937 0.879
Curiosity    0.605 0.366
Considerate  0.387 0.150

SS loadings:  1.62 
Proportion Var:  0.405 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.571 0.326
Obedience    0.996 0.991
Curiosity    0.583 0.340
Considerate  0.452 0.204

SS loadings:  1.861 
Proportion Var:  0.465 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.999 0.998
Obedience    0.452 0.204
Curiosity    0.601 0.361
Considerate  0.375 0.141

SS loadings:  1.703 
Proportion Var:  0.426 

Factor correlations: 

   F1
F1  1
          M2 df p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl       TLI
stats 180.79 14 0 0.08413544 0.07342042 0.09526139 0.08970581    0.08485678 0.08270939 0.6719754
            CFI
stats 0.7448698
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_metricb_2    8117.903 8153.936 8150.075 8204.766 -4042.952             
mod_configural_2 7908.410 7962.459 7956.668 8038.704 -3930.205 225.493  8 0
Iteration: 1, Log-Lik: -4196.320, Max-Change: 0.32538Iteration: 2, Log-Lik: -4068.345, Max-Change: 0.23133Iteration: 3, Log-Lik: -4021.578, Max-Change: 0.15942Iteration: 4, Log-Lik: -4007.620, Max-Change: 0.10714Iteration: 5, Log-Lik: -4001.834, Max-Change: 0.07467Iteration: 6, Log-Lik: -3998.640, Max-Change: 0.05366Iteration: 7, Log-Lik: -3995.458, Max-Change: 0.03775Iteration: 8, Log-Lik: -3994.368, Max-Change: 0.02164Iteration: 9, Log-Lik: -3993.785, Max-Change: 0.02112Iteration: 10, Log-Lik: -3993.498, Max-Change: 0.03959Iteration: 11, Log-Lik: -3992.289, Max-Change: 0.01209Iteration: 12, Log-Lik: -3992.127, Max-Change: 0.00939Iteration: 13, Log-Lik: -3991.954, Max-Change: 0.01098Iteration: 14, Log-Lik: -3991.842, Max-Change: 0.00776Iteration: 15, Log-Lik: -3991.778, Max-Change: 0.00681Iteration: 16, Log-Lik: -3991.749, Max-Change: 0.01397Iteration: 17, Log-Lik: -3991.594, Max-Change: 0.00623Iteration: 18, Log-Lik: -3991.570, Max-Change: 0.00517Iteration: 19, Log-Lik: -3991.544, Max-Change: 0.00586Iteration: 20, Log-Lik: -3991.525, Max-Change: 0.00452Iteration: 21, Log-Lik: -3991.513, Max-Change: 0.00409Iteration: 22, Log-Lik: -3991.506, Max-Change: 0.00679Iteration: 23, Log-Lik: -3991.477, Max-Change: 0.00423Iteration: 24, Log-Lik: -3991.470, Max-Change: 0.00356Iteration: 25, Log-Lik: -3991.462, Max-Change: 0.00388Iteration: 26, Log-Lik: -3991.456, Max-Change: 0.00317Iteration: 27, Log-Lik: -3991.451, Max-Change: 0.00290Iteration: 28, Log-Lik: -3991.448, Max-Change: 0.00481Iteration: 29, Log-Lik: -3991.438, Max-Change: 0.00319Iteration: 30, Log-Lik: -3991.434, Max-Change: 0.00271Iteration: 31, Log-Lik: -3991.430, Max-Change: 0.00288Iteration: 32, Log-Lik: -3991.427, Max-Change: 0.00242Iteration: 33, Log-Lik: -3991.424, Max-Change: 0.00223Iteration: 34, Log-Lik: -3991.422, Max-Change: 0.00363Iteration: 35, Log-Lik: -3991.417, Max-Change: 0.00250Iteration: 36, Log-Lik: -3991.415, Max-Change: 0.00214Iteration: 37, Log-Lik: -3991.412, Max-Change: 0.00225Iteration: 38, Log-Lik: -3991.410, Max-Change: 0.00191Iteration: 39, Log-Lik: -3991.409, Max-Change: 0.00177Iteration: 40, Log-Lik: -3991.407, Max-Change: 0.00283Iteration: 41, Log-Lik: -3991.404, Max-Change: 0.00200Iteration: 42, Log-Lik: -3991.403, Max-Change: 0.00171Iteration: 43, Log-Lik: -3991.401, Max-Change: 0.00179Iteration: 44, Log-Lik: -3991.400, Max-Change: 0.00152Iteration: 45, Log-Lik: -3991.399, Max-Change: 0.00141Iteration: 46, Log-Lik: -3991.398, Max-Change: 0.00225Iteration: 47, Log-Lik: -3991.396, Max-Change: 0.00161Iteration: 48, Log-Lik: -3991.395, Max-Change: 0.00136Iteration: 49, Log-Lik: -3991.395, Max-Change: 0.00142Iteration: 50, Log-Lik: -3991.394, Max-Change: 0.00121Iteration: 51, Log-Lik: -3991.393, Max-Change: 0.00112Iteration: 52, Log-Lik: -3991.392, Max-Change: 0.00178Iteration: 53, Log-Lik: -3991.391, Max-Change: 0.00128Iteration: 54, Log-Lik: -3991.391, Max-Change: 0.00108Iteration: 55, Log-Lik: -3991.390, Max-Change: 0.00114Iteration: 56, Log-Lik: -3991.390, Max-Change: 0.00096Iteration: 57, Log-Lik: -3991.389, Max-Change: 0.00089Iteration: 58, Log-Lik: -3991.389, Max-Change: 0.00142Iteration: 59, Log-Lik: -3991.388, Max-Change: 0.00102Iteration: 60, Log-Lik: -3991.388, Max-Change: 0.00086Iteration: 61, Log-Lik: -3991.387, Max-Change: 0.00092Iteration: 62, Log-Lik: -3991.387, Max-Change: 0.00077Iteration: 63, Log-Lik: -3991.387, Max-Change: 0.00071Iteration: 64, Log-Lik: -3991.387, Max-Change: 0.00113Iteration: 65, Log-Lik: -3991.386, Max-Change: 0.00081Iteration: 66, Log-Lik: -3991.386, Max-Change: 0.00069Iteration: 67, Log-Lik: -3991.386, Max-Change: 0.00073Iteration: 68, Log-Lik: -3991.386, Max-Change: 0.00061Iteration: 69, Log-Lik: -3991.385, Max-Change: 0.00057Iteration: 70, Log-Lik: -3991.385, Max-Change: 0.00089Iteration: 71, Log-Lik: -3991.385, Max-Change: 0.00064Iteration: 72, Log-Lik: -3991.385, Max-Change: 0.00055Iteration: 73, Log-Lik: -3991.385, Max-Change: 0.00058Iteration: 74, Log-Lik: -3991.385, Max-Change: 0.00049Iteration: 75, Log-Lik: -3991.384, Max-Change: 0.00046Iteration: 76, Log-Lik: -3991.384, Max-Change: 0.00071Iteration: 77, Log-Lik: -3991.384, Max-Change: 0.00051Iteration: 78, Log-Lik: -3991.384, Max-Change: 0.00044Iteration: 79, Log-Lik: -3991.384, Max-Change: 0.00046Iteration: 80, Log-Lik: -3991.384, Max-Change: 0.00039Iteration: 81, Log-Lik: -3991.384, Max-Change: 0.00036Iteration: 82, Log-Lik: -3991.384, Max-Change: 0.00056Iteration: 83, Log-Lik: -3991.384, Max-Change: 0.00041Iteration: 84, Log-Lik: -3991.384, Max-Change: 0.00035Iteration: 85, Log-Lik: -3991.384, Max-Change: 0.00036Iteration: 86, Log-Lik: -3991.383, Max-Change: 0.00031Iteration: 87, Log-Lik: -3991.383, Max-Change: 0.00029Iteration: 88, Log-Lik: -3991.383, Max-Change: 0.00044Iteration: 89, Log-Lik: -3991.383, Max-Change: 0.00033Iteration: 90, Log-Lik: -3991.383, Max-Change: 0.00028Iteration: 91, Log-Lik: -3991.383, Max-Change: 0.00029Iteration: 92, Log-Lik: -3991.383, Max-Change: 0.00024Iteration: 93, Log-Lik: -3991.383, Max-Change: 0.00023Iteration: 94, Log-Lik: -3991.383, Max-Change: 0.00035Iteration: 95, Log-Lik: -3991.383, Max-Change: 0.00026Iteration: 96, Log-Lik: -3991.383, Max-Change: 0.00022Iteration: 97, Log-Lik: -3991.383, Max-Change: 0.00022Iteration: 98, Log-Lik: -3991.383, Max-Change: 0.00019Iteration: 99, Log-Lik: -3991.383, Max-Change: 0.00018Iteration: 100, Log-Lik: -3991.383, Max-Change: 0.00027Iteration: 101, Log-Lik: -3991.383, Max-Change: 0.00020Iteration: 102, Log-Lik: -3991.383, Max-Change: 0.00017Iteration: 103, Log-Lik: -3991.383, Max-Change: 0.00018Iteration: 104, Log-Lik: -3991.383, Max-Change: 0.00015Iteration: 105, Log-Lik: -3991.383, Max-Change: 0.00014Iteration: 106, Log-Lik: -3991.383, Max-Change: 0.00021Iteration: 107, Log-Lik: -3991.383, Max-Change: 0.00016Iteration: 108, Log-Lik: -3991.383, Max-Change: 0.00014Iteration: 109, Log-Lik: -3991.383, Max-Change: 0.00015Iteration: 110, Log-Lik: -3991.383, Max-Change: 0.00012Iteration: 111, Log-Lik: -3991.383, Max-Change: 0.00011Iteration: 112, Log-Lik: -3991.383, Max-Change: 0.00017Iteration: 113, Log-Lik: -3991.383, Max-Change: 0.00013Iteration: 114, Log-Lik: -3991.383, Max-Change: 0.00011Iteration: 115, Log-Lik: -3991.383, Max-Change: 0.00012Iteration: 116, Log-Lik: -3991.383, Max-Change: 0.00010

Call:
multipleGroup(data = irt_data_2, model = 1, group = group_2, 
    invariance = c("slopes", "intercepts", "free_var", "free_means"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 116 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -3991.383
Estimated parameters: 28 
AIC = 8006.766
BIC = 8071.913; SABIC = 8033.79
G2 (33) = 158.9, p = 0
RMSEA = 0.048, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.732 0.535
Obedience    0.684 0.467
Curiosity    0.545 0.297
Considerate  0.379 0.144

SS loadings:  1.444 
Proportion Var:  0.361 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.806 0.650
Obedience    0.766 0.586
Curiosity    0.637 0.406
Considerate  0.462 0.213

SS loadings:  1.856 
Proportion Var:  0.464 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.704 0.496
Obedience    0.655 0.429
Curiosity    0.515 0.266
Considerate  0.354 0.126

SS loadings:  1.316 
Proportion Var:  0.329 

Factor correlations: 

   F1
F1  1
           M2 df p      RMSEA    RMSEA_5  RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl      TLI
stats 141.648 18 0 0.06388739 0.05431763 0.0738569 0.09271736     0.0614687 0.09091321 0.810862
           CFI
stats 0.810862
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar2_2    8006.766 8033.790 8030.895 8071.913 -3991.383             
mod_configural_2 7908.410 7962.459 7956.668 8038.704 -3930.205 122.356 12 0
Iteration: 1, Log-Lik: -4196.320, Max-Change: 0.32539Iteration: 2, Log-Lik: -4135.421, Max-Change: 0.22549Iteration: 3, Log-Lik: -4113.561, Max-Change: 0.13372Iteration: 4, Log-Lik: -4107.144, Max-Change: 0.07504Iteration: 5, Log-Lik: -4105.153, Max-Change: 0.04819Iteration: 6, Log-Lik: -4104.412, Max-Change: 0.03530Iteration: 7, Log-Lik: -4104.014, Max-Change: 0.02694Iteration: 8, Log-Lik: -4103.857, Max-Change: 0.02184Iteration: 9, Log-Lik: -4103.748, Max-Change: 0.01828Iteration: 10, Log-Lik: -4103.653, Max-Change: 0.01833Iteration: 11, Log-Lik: -4103.583, Max-Change: 0.01406Iteration: 12, Log-Lik: -4103.538, Max-Change: 0.01135Iteration: 13, Log-Lik: -4103.500, Max-Change: 0.01067Iteration: 14, Log-Lik: -4103.473, Max-Change: 0.00829Iteration: 15, Log-Lik: -4103.454, Max-Change: 0.00679Iteration: 16, Log-Lik: -4103.437, Max-Change: 0.00704Iteration: 17, Log-Lik: -4103.423, Max-Change: 0.00563Iteration: 18, Log-Lik: -4103.414, Max-Change: 0.00475Iteration: 19, Log-Lik: -4103.407, Max-Change: 0.00466Iteration: 20, Log-Lik: -4103.401, Max-Change: 0.00389Iteration: 21, Log-Lik: -4103.397, Max-Change: 0.00338Iteration: 22, Log-Lik: -4103.393, Max-Change: 0.00399Iteration: 23, Log-Lik: -4103.389, Max-Change: 0.00314Iteration: 24, Log-Lik: -4103.387, Max-Change: 0.00264Iteration: 25, Log-Lik: -4103.385, Max-Change: 0.00260Iteration: 26, Log-Lik: -4103.383, Max-Change: 0.00220Iteration: 27, Log-Lik: -4103.382, Max-Change: 0.00195Iteration: 28, Log-Lik: -4103.381, Max-Change: 0.00255Iteration: 29, Log-Lik: -4103.379, Max-Change: 0.00195Iteration: 30, Log-Lik: -4103.378, Max-Change: 0.00163Iteration: 31, Log-Lik: -4103.377, Max-Change: 0.00154Iteration: 32, Log-Lik: -4103.377, Max-Change: 0.00134Iteration: 33, Log-Lik: -4103.376, Max-Change: 0.00121Iteration: 34, Log-Lik: -4103.376, Max-Change: 0.00169Iteration: 35, Log-Lik: -4103.375, Max-Change: 0.00127Iteration: 36, Log-Lik: -4103.375, Max-Change: 0.00106Iteration: 37, Log-Lik: -4103.374, Max-Change: 0.00100Iteration: 38, Log-Lik: -4103.374, Max-Change: 0.00088Iteration: 39, Log-Lik: -4103.374, Max-Change: 0.00080Iteration: 40, Log-Lik: -4103.374, Max-Change: 0.00116Iteration: 41, Log-Lik: -4103.373, Max-Change: 0.00087Iteration: 42, Log-Lik: -4103.373, Max-Change: 0.00072Iteration: 43, Log-Lik: -4103.373, Max-Change: 0.00069Iteration: 44, Log-Lik: -4103.373, Max-Change: 0.00061Iteration: 45, Log-Lik: -4103.373, Max-Change: 0.00056Iteration: 46, Log-Lik: -4103.373, Max-Change: 0.00082Iteration: 47, Log-Lik: -4103.372, Max-Change: 0.00061Iteration: 48, Log-Lik: -4103.372, Max-Change: 0.00051Iteration: 49, Log-Lik: -4103.372, Max-Change: 0.00049Iteration: 50, Log-Lik: -4103.372, Max-Change: 0.00043Iteration: 51, Log-Lik: -4103.372, Max-Change: 0.00040Iteration: 52, Log-Lik: -4103.372, Max-Change: 0.00060Iteration: 53, Log-Lik: -4103.372, Max-Change: 0.00043Iteration: 54, Log-Lik: -4103.372, Max-Change: 0.00037Iteration: 55, Log-Lik: -4103.372, Max-Change: 0.00035Iteration: 56, Log-Lik: -4103.372, Max-Change: 0.00031Iteration: 57, Log-Lik: -4103.372, Max-Change: 0.00029Iteration: 58, Log-Lik: -4103.372, Max-Change: 0.00046Iteration: 59, Log-Lik: -4103.372, Max-Change: 0.00031Iteration: 60, Log-Lik: -4103.372, Max-Change: 0.00027Iteration: 61, Log-Lik: -4103.372, Max-Change: 0.00026Iteration: 62, Log-Lik: -4103.372, Max-Change: 0.00023Iteration: 63, Log-Lik: -4103.371, Max-Change: 0.00021Iteration: 64, Log-Lik: -4103.371, Max-Change: 0.00034Iteration: 65, Log-Lik: -4103.371, Max-Change: 0.00024Iteration: 66, Log-Lik: -4103.371, Max-Change: 0.00019Iteration: 67, Log-Lik: -4103.371, Max-Change: 0.00019Iteration: 68, Log-Lik: -4103.371, Max-Change: 0.00017Iteration: 69, Log-Lik: -4103.371, Max-Change: 0.00015Iteration: 70, Log-Lik: -4103.371, Max-Change: 0.00026Iteration: 71, Log-Lik: -4103.371, Max-Change: 0.00018Iteration: 72, Log-Lik: -4103.371, Max-Change: 0.00014Iteration: 73, Log-Lik: -4103.371, Max-Change: 0.00014Iteration: 74, Log-Lik: -4103.371, Max-Change: 0.00012Iteration: 75, Log-Lik: -4103.371, Max-Change: 0.00012Iteration: 76, Log-Lik: -4103.371, Max-Change: 0.00022Iteration: 77, Log-Lik: -4103.371, Max-Change: 0.00012Iteration: 78, Log-Lik: -4103.371, Max-Change: 0.00011Iteration: 79, Log-Lik: -4103.371, Max-Change: 0.00011Iteration: 80, Log-Lik: -4103.371, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_2, model = 1, group = group_2, 
    invariance = c("slopes", "intercepts", "free_var"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 80 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4103.371
Estimated parameters: 26 
AIC = 8226.743
BIC = 8281.032; SABIC = 8249.263
G2 (35) = 382.88, p = 0
RMSEA = 0.077, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.660 0.436
Obedience    0.663 0.440
Curiosity    0.715 0.511
Considerate  0.476 0.227

SS loadings:  1.614 
Proportion Var:  0.404 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.757 0.574
Obedience    0.760 0.577
Curiosity    0.803 0.645
Considerate  0.581 0.338

SS loadings:  2.134 
Proportion Var:  0.534 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.666 0.443
Obedience    0.669 0.447
Curiosity    0.720 0.519
Considerate  0.482 0.232

SS loadings:  1.642 
Proportion Var:  0.41 

Factor correlations: 

   F1
F1  1
            M2 df p     RMSEA   RMSEA_5  RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl       TLI
stats 365.9373 20 0 0.1013775 0.0923998 0.1105704 0.06038159    0.06629187 0.07438899 0.5237536
            CFI
stats 0.4708373
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar1_2    8226.743 8249.263 8246.850 8281.032 -4103.371             
mod_configural_2 7908.410 7962.459 7956.668 8038.704 -3930.205 346.333 14 0
Iteration: 1, Log-Lik: -4196.320, Max-Change: 0.32539Iteration: 2, Log-Lik: -4144.238, Max-Change: 0.25259Iteration: 3, Log-Lik: -4121.929, Max-Change: 0.17649Iteration: 4, Log-Lik: -4113.372, Max-Change: 0.11706Iteration: 5, Log-Lik: -4110.173, Max-Change: 0.07565Iteration: 6, Log-Lik: -4108.978, Max-Change: 0.04829Iteration: 7, Log-Lik: -4108.352, Max-Change: 0.01915Iteration: 8, Log-Lik: -4108.292, Max-Change: 0.01211Iteration: 9, Log-Lik: -4108.269, Max-Change: 0.00744Iteration: 10, Log-Lik: -4108.257, Max-Change: 0.00475Iteration: 11, Log-Lik: -4108.255, Max-Change: 0.00287Iteration: 12, Log-Lik: -4108.254, Max-Change: 0.00249Iteration: 13, Log-Lik: -4108.253, Max-Change: 0.00075Iteration: 14, Log-Lik: -4108.253, Max-Change: 0.00019Iteration: 15, Log-Lik: -4108.253, Max-Change: 0.00051Iteration: 16, Log-Lik: -4108.253, Max-Change: 0.00021Iteration: 17, Log-Lik: -4108.253, Max-Change: 0.00010Iteration: 18, Log-Lik: -4108.253, Max-Change: 0.00024Iteration: 19, Log-Lik: -4108.253, Max-Change: 0.00014Iteration: 20, Log-Lik: -4108.253, Max-Change: 0.00007

Call:
multipleGroup(data = irt_data_2, model = 1, group = group_2, 
    invariance = c("slopes", "intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 20 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4108.253
Estimated parameters: 24 
AIC = 8232.505
BIC = 8275.936; SABIC = 8250.522
G2 (37) = 392.64, p = 0
RMSEA = 0.076, CFI = NaN, TLI = NaN

----------
GROUP: Boy  
                F1    h2
Independence 0.699 0.489
Obedience    0.691 0.477
Curiosity    0.751 0.564
Considerate  0.524 0.275

SS loadings:  1.805 
Proportion Var:  0.451 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Independence 0.699 0.489
Obedience    0.691 0.477
Curiosity    0.751 0.564
Considerate  0.524 0.275

SS loadings:  1.805 
Proportion Var:  0.451 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Independence 0.699 0.489
Obedience    0.691 0.477
Curiosity    0.751 0.564
Considerate  0.524 0.275

SS loadings:  1.805 
Proportion Var:  0.451 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5  RMSEA_95 SRMSR.Boy. SRMSR.Control SRMSR.Girl       TLI
stats 375.9426 22 0 0.09777167 0.08919996 0.1065436 0.07374458    0.04513198 0.08782176 0.5570298
           CFI
stats 0.458592
                         AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_fullconstrain_2 8232.505 8250.522 8248.591 8275.936 -4108.253             
mod_configural_2    7908.410 7962.459 7956.668 8038.704 -3930.205 356.095 16 0
                      AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_metric_2     7903.596 7939.629 7935.768 7990.459 -3935.798                
mod_configural_2 7908.410 7962.459 7956.668 8038.704 -3930.205 11.186  8 0.191
                   AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_scalar2_2 8006.766 8033.790 8030.895 8071.913 -3991.383            
mod_metric_2  7903.596 7939.629 7935.768 7990.459 -3935.798 111.17  4 0
                   AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar1_2 8226.743 8249.263 8246.850 8281.032 -4103.371             
mod_scalar2_2 8006.766 8033.790 8030.895 8071.913 -3991.383 223.977  2 0
                         AIC    SABIC       HQ      BIC    logLik    X2 df     p
mod_fullconstrain_2 8232.505 8250.522 8248.591 8275.936 -4108.253               
mod_scalar1_2       8226.743 8249.263 8246.850 8281.032 -4103.371 9.762  2 0.008
                         AIC    SABIC       HQ      BIC    logLik      X2 df     p
mod_fullconstrain_2 8232.505 8250.522 8248.591 8275.936 -4108.253                 
mod_scalar1_2       8226.743 8249.263 8246.850 8281.032 -4103.371   9.762  2 0.008
mod_scalar2_2       8006.766 8033.790 8030.895 8071.913 -3991.383 223.977  2     0
mod_metric_2        7903.596 7939.629 7935.768 7990.459 -3935.798  111.17  4     0
mod_configural_2    7908.410 7962.459 7956.668 8038.704 -3930.205  11.186  8 0.191
################### Nested Model Comparison #########################

Scaled Chi-Squared Difference Test (method = “satorra.2000”)

lavaan->unknown():  
   lavaan NOTE: The “Chisq” column contains standard test statistics, not the robust test that 
   should be reported per model. A robust difference test is a function of two standard (not 
   robust) statistics.
                    Df AIC BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
meq.list.configural 60         226.25                                  
meq.list.strong     72         415.83    227.123      12  < 2.2e-16 ***
meq.list.means      74         678.18    103.099       2  < 2.2e-16 ***
meq.list.strict     90         742.13     48.825      16  3.521e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

####################### Model Fit Indices ###########################
                    chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled tli.scaled  srmr
meq.list.configural     308.642†        60          .000        .072†      .950†      .929† .075†
meq.list.strong         549.369         72          .000        .091       .903       .887  .079 
meq.list.means          800.013         74          .000        .111       .853       .833  .082 
meq.list.strict         764.948         90          .000        .097       .863       .872  .081 

################## Differences in Fit Indices #######################
                                      df.scaled rmsea.scaled cfi.scaled tli.scaled   srmr
meq.list.strong - meq.list.configural        12        0.019     -0.046     -0.042  0.003
meq.list.means - meq.list.strong              2        0.020     -0.050     -0.054  0.003
meq.list.strict - meq.list.means             16       -0.014      0.010      0.039 -0.001

group_3
  1   2   3 
798 805 805 
Iteration: 1, Log-Lik: -11538.508, Max-Change: 0.72724Iteration: 2, Log-Lik: -11172.776, Max-Change: 0.28202Iteration: 3, Log-Lik: -11107.203, Max-Change: 0.17221Iteration: 4, Log-Lik: -11086.691, Max-Change: 0.10884Iteration: 5, Log-Lik: -11079.394, Max-Change: 0.07050Iteration: 6, Log-Lik: -11076.532, Max-Change: 0.04557Iteration: 7, Log-Lik: -11074.768, Max-Change: 0.01702Iteration: 8, Log-Lik: -11074.510, Max-Change: 0.01299Iteration: 9, Log-Lik: -11074.373, Max-Change: 0.00916Iteration: 10, Log-Lik: -11074.243, Max-Change: 0.00431Iteration: 11, Log-Lik: -11074.227, Max-Change: 0.00283Iteration: 12, Log-Lik: -11074.219, Max-Change: 0.00223Iteration: 13, Log-Lik: -11074.209, Max-Change: 0.00122Iteration: 14, Log-Lik: -11074.208, Max-Change: 0.00087Iteration: 15, Log-Lik: -11074.207, Max-Change: 0.00068Iteration: 16, Log-Lik: -11074.207, Max-Change: 0.00014Iteration: 17, Log-Lik: -11074.207, Max-Change: 0.00013Iteration: 18, Log-Lik: -11074.207, Max-Change: 0.00010Iteration: 19, Log-Lik: -11074.207, Max-Change: 0.00006

Call:
multipleGroup(data = irt_data_3, model = 1, group = group_3)

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 19 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -11074.21
Estimated parameters: 48 
AIC = 22244.41
BIC = 22522.17; SABIC = 22369.66
G2 (717) = 1121.29, p = 0
RMSEA = 0.015, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.536 0.287
Obedient     0.732 0.535
GoodMannered 0.664 0.441
WellBehaved  0.602 0.363
Polite       0.701 0.492
Orderly      0.740 0.547
Disciplined  0.679 0.461
Loyal        0.659 0.434

SS loadings:  3.56 
Proportion Var:  0.445 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.625 0.391
Obedient     0.732 0.535
GoodMannered 0.730 0.532
WellBehaved  0.596 0.355
Polite       0.677 0.459
Orderly      0.755 0.570
Disciplined  0.732 0.535
Loyal        0.575 0.331

SS loadings:  3.708 
Proportion Var:  0.464 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.399 0.159
Obedient     0.673 0.453
GoodMannered 0.684 0.468
WellBehaved  0.625 0.390
Polite       0.582 0.339
Orderly      0.768 0.590
Disciplined  0.718 0.516
Loyal        0.582 0.339

SS loadings:  3.253 
Proportion Var:  0.407 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control SRMSR.Girl       TLI
stats 324.2211 60 0 0.04277304 0.03826151 0.04738867 0.05514797    0.05392601 0.05365492 0.9228775
            CFI
stats 0.9449125
Iteration: 1, Log-Lik: -11538.508, Max-Change: 0.61656Iteration: 2, Log-Lik: -11323.899, Max-Change: 0.49787Iteration: 3, Log-Lik: -11264.208, Max-Change: 0.23626Iteration: 4, Log-Lik: -11247.670, Max-Change: 0.17195Iteration: 5, Log-Lik: -11240.596, Max-Change: 0.14562Iteration: 6, Log-Lik: -11238.018, Max-Change: 0.09047Iteration: 7, Log-Lik: -11237.219, Max-Change: 0.05735Iteration: 8, Log-Lik: -11236.860, Max-Change: 0.04193Iteration: 9, Log-Lik: -11236.702, Max-Change: 0.02994Iteration: 10, Log-Lik: -11236.563, Max-Change: 0.01409Iteration: 11, Log-Lik: -11236.548, Max-Change: 0.01022Iteration: 12, Log-Lik: -11236.538, Max-Change: 0.00532Iteration: 13, Log-Lik: -11236.533, Max-Change: 0.00588Iteration: 14, Log-Lik: -11236.529, Max-Change: 0.00126Iteration: 15, Log-Lik: -11236.527, Max-Change: 0.00124Iteration: 16, Log-Lik: -11236.523, Max-Change: 0.00185Iteration: 17, Log-Lik: -11236.523, Max-Change: 0.00020Iteration: 18, Log-Lik: -11236.523, Max-Change: 0.00014Iteration: 19, Log-Lik: -11236.522, Max-Change: 0.00071Iteration: 20, Log-Lik: -11236.522, Max-Change: 0.00072Iteration: 21, Log-Lik: -11236.522, Max-Change: 0.00066Iteration: 22, Log-Lik: -11236.522, Max-Change: 0.00014Iteration: 23, Log-Lik: -11236.522, Max-Change: 0.00065Iteration: 24, Log-Lik: -11236.522, Max-Change: 0.00011Iteration: 25, Log-Lik: -11236.522, Max-Change: 0.00058Iteration: 26, Log-Lik: -11236.522, Max-Change: 0.00018Iteration: 27, Log-Lik: -11236.522, Max-Change: 0.00057Iteration: 28, Log-Lik: -11236.522, Max-Change: 0.00065Iteration: 29, Log-Lik: -11236.522, Max-Change: 0.00012Iteration: 30, Log-Lik: -11236.522, Max-Change: 0.00045Iteration: 31, Log-Lik: -11236.522, Max-Change: 0.00014Iteration: 32, Log-Lik: -11236.522, Max-Change: 0.00040Iteration: 33, Log-Lik: -11236.522, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_3, model = 1, group = group_3, 
    invariance = c("intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 33 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -11236.52
Estimated parameters: 48 
AIC = 22537.04
BIC = 22722.21; SABIC = 22620.54
G2 (733) = 1445.92, p = 0
RMSEA = 0.02, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.572 0.327
Obedient     0.612 0.374
GoodMannered 0.651 0.424
WellBehaved  0.566 0.321
Polite       0.760 0.578
Orderly      0.808 0.654
Disciplined  0.639 0.409
Loyal        0.653 0.426

SS loadings:  3.513 
Proportion Var:  0.439 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.636 0.405
Obedient     0.857 0.734
GoodMannered 0.720 0.518
WellBehaved  0.647 0.418
Polite       0.649 0.421
Orderly      0.669 0.448
Disciplined  0.746 0.557
Loyal        0.515 0.265

SS loadings:  3.765 
Proportion Var:  0.471 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.423 0.179
Obedient     0.692 0.478
GoodMannered 0.662 0.438
WellBehaved  0.619 0.383
Polite       0.565 0.320
Orderly      0.769 0.592
Disciplined  0.777 0.603
Loyal        0.599 0.359

SS loadings:  3.352 
Proportion Var:  0.419 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control SRMSR.Girl       TLI
stats 672.5952 76 0 0.05710775 0.05317301 0.06110135 0.05979684    0.06247862 0.05692781 0.8625228
            CFI
stats 0.8756158
                      AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_metricb_3    22537.04 22620.54 22604.40 22722.21 -11236.52            
mod_configural_3 22244.41 22369.66 22345.44 22522.17 -11074.21 324.63 16 0
Iteration: 1, Log-Lik: -11538.508, Max-Change: 0.73940Iteration: 2, Log-Lik: -11187.560, Max-Change: 0.27062Iteration: 3, Log-Lik: -11119.566, Max-Change: 0.16775Iteration: 4, Log-Lik: -11098.383, Max-Change: 0.10290Iteration: 5, Log-Lik: -11090.716, Max-Change: 0.06755Iteration: 6, Log-Lik: -11087.806, Max-Change: 0.04131Iteration: 7, Log-Lik: -11086.115, Max-Change: 0.02215Iteration: 8, Log-Lik: -11085.834, Max-Change: 0.01320Iteration: 9, Log-Lik: -11085.698, Max-Change: 0.01024Iteration: 10, Log-Lik: -11085.560, Max-Change: 0.00415Iteration: 11, Log-Lik: -11085.547, Max-Change: 0.00237Iteration: 12, Log-Lik: -11085.544, Max-Change: 0.00145Iteration: 13, Log-Lik: -11085.540, Max-Change: 0.00119Iteration: 14, Log-Lik: -11085.539, Max-Change: 0.00090Iteration: 15, Log-Lik: -11085.538, Max-Change: 0.00044Iteration: 16, Log-Lik: -11085.538, Max-Change: 0.00038Iteration: 17, Log-Lik: -11085.538, Max-Change: 0.00029Iteration: 18, Log-Lik: -11085.538, Max-Change: 0.00021Iteration: 19, Log-Lik: -11085.538, Max-Change: 0.00017Iteration: 20, Log-Lik: -11085.538, Max-Change: 0.00013Iteration: 21, Log-Lik: -11085.538, Max-Change: 0.00010

Call:
multipleGroup(data = irt_data_3, model = 1, group = group_3, 
    invariance = c("slopes"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 21 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -11085.54
Estimated parameters: 48 
AIC = 22235.08
BIC = 22420.24; SABIC = 22318.57
G2 (733) = 1143.95, p = 0
RMSEA = 0.015, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.523 0.274
Obedient     0.710 0.504
GoodMannered 0.693 0.481
WellBehaved  0.606 0.367
Polite       0.658 0.434
Orderly      0.756 0.571
Disciplined  0.712 0.507
Loyal        0.604 0.365

SS loadings:  3.502 
Proportion Var:  0.438 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.523 0.274
Obedient     0.710 0.504
GoodMannered 0.693 0.481
WellBehaved  0.606 0.367
Polite       0.658 0.434
Orderly      0.756 0.571
Disciplined  0.712 0.507
Loyal        0.604 0.365

SS loadings:  3.502 
Proportion Var:  0.438 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.523 0.274
Obedient     0.710 0.504
GoodMannered 0.693 0.481
WellBehaved  0.606 0.367
Polite       0.658 0.434
Orderly      0.756 0.571
Disciplined  0.712 0.507
Loyal        0.604 0.365

SS loadings:  3.502 
Proportion Var:  0.438 

Factor correlations: 

   F1
F1  1
            M2 df p    RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control SRMSR.Girl       TLI
stats 349.0982 76 0 0.038638 0.03457751 0.04278072 0.05718736    0.05935374 0.06214806 0.9370682
            CFI
stats 0.9430617
                      AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_metric_3     22235.08 22318.57 22302.43 22420.24 -11085.54                
mod_configural_3 22244.41 22369.66 22345.44 22522.17 -11074.21 22.662 16 0.123
Iteration: 1, Log-Lik: -11538.508, Max-Change: 0.38871Iteration: 2, Log-Lik: -11301.332, Max-Change: 0.18234Iteration: 3, Log-Lik: -11266.240, Max-Change: 0.08926Iteration: 4, Log-Lik: -11256.360, Max-Change: 0.05109Iteration: 5, Log-Lik: -11250.341, Max-Change: 0.04631Iteration: 6, Log-Lik: -11245.702, Max-Change: 0.04006Iteration: 7, Log-Lik: -11241.951, Max-Change: 0.03426Iteration: 8, Log-Lik: -11238.878, Max-Change: 0.03170Iteration: 9, Log-Lik: -11236.348, Max-Change: 0.02918Iteration: 10, Log-Lik: -11232.907, Max-Change: 0.06531Iteration: 11, Log-Lik: -11228.131, Max-Change: 0.03122Iteration: 12, Log-Lik: -11227.275, Max-Change: 0.01950Iteration: 13, Log-Lik: -11226.410, Max-Change: 0.01771Iteration: 14, Log-Lik: -11225.933, Max-Change: 0.01328Iteration: 15, Log-Lik: -11225.571, Max-Change: 0.01111Iteration: 16, Log-Lik: -11225.080, Max-Change: 0.02296Iteration: 17, Log-Lik: -11224.385, Max-Change: 0.01136Iteration: 18, Log-Lik: -11224.253, Max-Change: 0.00724Iteration: 19, Log-Lik: -11224.116, Max-Change: 0.00665Iteration: 20, Log-Lik: -11224.041, Max-Change: 0.00499Iteration: 21, Log-Lik: -11223.984, Max-Change: 0.00448Iteration: 22, Log-Lik: -11223.905, Max-Change: 0.00905Iteration: 23, Log-Lik: -11223.794, Max-Change: 0.00431Iteration: 24, Log-Lik: -11223.773, Max-Change: 0.00275Iteration: 25, Log-Lik: -11223.750, Max-Change: 0.00251Iteration: 26, Log-Lik: -11223.738, Max-Change: 0.00191Iteration: 27, Log-Lik: -11223.729, Max-Change: 0.00185Iteration: 28, Log-Lik: -11223.715, Max-Change: 0.00374Iteration: 29, Log-Lik: -11223.697, Max-Change: 0.00164Iteration: 30, Log-Lik: -11223.693, Max-Change: 0.00104Iteration: 31, Log-Lik: -11223.689, Max-Change: 0.00095Iteration: 32, Log-Lik: -11223.687, Max-Change: 0.00078Iteration: 33, Log-Lik: -11223.686, Max-Change: 0.00076Iteration: 34, Log-Lik: -11223.684, Max-Change: 0.00158Iteration: 35, Log-Lik: -11223.680, Max-Change: 0.00060Iteration: 36, Log-Lik: -11223.680, Max-Change: 0.00038Iteration: 37, Log-Lik: -11223.679, Max-Change: 0.00039Iteration: 38, Log-Lik: -11223.679, Max-Change: 0.00033Iteration: 39, Log-Lik: -11223.679, Max-Change: 0.00032Iteration: 40, Log-Lik: -11223.678, Max-Change: 0.00068Iteration: 41, Log-Lik: -11223.678, Max-Change: 0.00021Iteration: 42, Log-Lik: -11223.678, Max-Change: 0.00016Iteration: 43, Log-Lik: -11223.677, Max-Change: 0.00018Iteration: 44, Log-Lik: -11223.677, Max-Change: 0.00014Iteration: 45, Log-Lik: -11223.677, Max-Change: 0.00013Iteration: 46, Log-Lik: -11223.677, Max-Change: 0.00030Iteration: 47, Log-Lik: -11223.677, Max-Change: 0.00007

Call:
multipleGroup(data = irt_data_3, model = 1, group = group_3, 
    invariance = c("slopes", "intercepts", "free_var", "free_means"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 47 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -11223.68
Estimated parameters: 52 
AIC = 22487.35
BIC = 22603.09; SABIC = 22539.54
G2 (745) = 1420.23, p = 0
RMSEA = 0.019, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.565 0.319
Obedient     0.717 0.515
GoodMannered 0.679 0.461
WellBehaved  0.610 0.372
Polite       0.674 0.454
Orderly      0.709 0.503
Disciplined  0.728 0.529
Loyal        0.563 0.317

SS loadings:  3.47 
Proportion Var:  0.434 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.584 0.341
Obedient     0.734 0.539
GoodMannered 0.697 0.486
WellBehaved  0.629 0.395
Polite       0.692 0.479
Orderly      0.727 0.528
Disciplined  0.744 0.554
Loyal        0.582 0.339

SS loadings:  3.661 
Proportion Var:  0.458 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.528 0.279
Obedient     0.683 0.467
GoodMannered 0.643 0.414
WellBehaved  0.573 0.328
Polite       0.638 0.407
Orderly      0.675 0.455
Disciplined  0.694 0.481
Loyal        0.526 0.277

SS loadings:  3.108 
Proportion Var:  0.389 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control SRMSR.Girl       TLI
stats 658.5305 88 0 0.05189913 0.04821907 0.05563341 0.06242843    0.05704103 0.06419632 0.8864569
            CFI
stats 0.8810501
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar2_3    22487.35 22539.54 22529.45 22603.08 -11223.68             
mod_configural_3 22244.41 22369.66 22345.44 22522.17 -11074.21 298.941 28 0
Iteration: 1, Log-Lik: -11538.508, Max-Change: 0.38870Iteration: 2, Log-Lik: -11319.142, Max-Change: 0.17754Iteration: 3, Log-Lik: -11290.653, Max-Change: 0.08494Iteration: 4, Log-Lik: -11286.004, Max-Change: 0.04721Iteration: 5, Log-Lik: -11284.260, Max-Change: 0.03708Iteration: 6, Log-Lik: -11283.125, Max-Change: 0.03310Iteration: 7, Log-Lik: -11281.803, Max-Change: 0.05838Iteration: 8, Log-Lik: -11280.705, Max-Change: 0.03282Iteration: 9, Log-Lik: -11280.150, Max-Change: 0.02528Iteration: 10, Log-Lik: -11279.448, Max-Change: 0.03853Iteration: 11, Log-Lik: -11278.886, Max-Change: 0.02312Iteration: 12, Log-Lik: -11278.558, Max-Change: 0.01840Iteration: 13, Log-Lik: -11278.156, Max-Change: 0.03447Iteration: 14, Log-Lik: -11277.679, Max-Change: 0.01833Iteration: 15, Log-Lik: -11277.481, Max-Change: 0.01365Iteration: 16, Log-Lik: -11277.249, Max-Change: 0.01806Iteration: 17, Log-Lik: -11277.083, Max-Change: 0.01192Iteration: 18, Log-Lik: -11276.969, Max-Change: 0.00988Iteration: 19, Log-Lik: -11276.867, Max-Change: 0.02254Iteration: 20, Log-Lik: -11276.620, Max-Change: 0.01089Iteration: 21, Log-Lik: -11276.548, Max-Change: 0.00759Iteration: 22, Log-Lik: -11276.483, Max-Change: 0.00781Iteration: 23, Log-Lik: -11276.437, Max-Change: 0.00604Iteration: 24, Log-Lik: -11276.399, Max-Change: 0.00532Iteration: 25, Log-Lik: -11276.366, Max-Change: 0.01271Iteration: 26, Log-Lik: -11276.279, Max-Change: 0.00615Iteration: 27, Log-Lik: -11276.254, Max-Change: 0.00426Iteration: 28, Log-Lik: -11276.233, Max-Change: 0.00426Iteration: 29, Log-Lik: -11276.218, Max-Change: 0.00336Iteration: 30, Log-Lik: -11276.205, Max-Change: 0.00299Iteration: 31, Log-Lik: -11276.194, Max-Change: 0.00720Iteration: 32, Log-Lik: -11276.164, Max-Change: 0.00349Iteration: 33, Log-Lik: -11276.156, Max-Change: 0.00243Iteration: 34, Log-Lik: -11276.149, Max-Change: 0.00241Iteration: 35, Log-Lik: -11276.143, Max-Change: 0.00191Iteration: 36, Log-Lik: -11276.139, Max-Change: 0.00170Iteration: 37, Log-Lik: -11276.135, Max-Change: 0.00412Iteration: 38, Log-Lik: -11276.125, Max-Change: 0.00200Iteration: 39, Log-Lik: -11276.122, Max-Change: 0.00139Iteration: 40, Log-Lik: -11276.120, Max-Change: 0.00138Iteration: 41, Log-Lik: -11276.118, Max-Change: 0.00110Iteration: 42, Log-Lik: -11276.117, Max-Change: 0.00098Iteration: 43, Log-Lik: -11276.115, Max-Change: 0.00237Iteration: 44, Log-Lik: -11276.112, Max-Change: 0.00115Iteration: 45, Log-Lik: -11276.111, Max-Change: 0.00080Iteration: 46, Log-Lik: -11276.110, Max-Change: 0.00079Iteration: 47, Log-Lik: -11276.110, Max-Change: 0.00063Iteration: 48, Log-Lik: -11276.109, Max-Change: 0.00057Iteration: 49, Log-Lik: -11276.109, Max-Change: 0.00137Iteration: 50, Log-Lik: -11276.108, Max-Change: 0.00067Iteration: 51, Log-Lik: -11276.107, Max-Change: 0.00047Iteration: 52, Log-Lik: -11276.107, Max-Change: 0.00047Iteration: 53, Log-Lik: -11276.107, Max-Change: 0.00037Iteration: 54, Log-Lik: -11276.107, Max-Change: 0.00033Iteration: 55, Log-Lik: -11276.107, Max-Change: 0.00079Iteration: 56, Log-Lik: -11276.106, Max-Change: 0.00039Iteration: 57, Log-Lik: -11276.106, Max-Change: 0.00027Iteration: 58, Log-Lik: -11276.106, Max-Change: 0.00027Iteration: 59, Log-Lik: -11276.106, Max-Change: 0.00021Iteration: 60, Log-Lik: -11276.106, Max-Change: 0.00019Iteration: 61, Log-Lik: -11276.106, Max-Change: 0.00046Iteration: 62, Log-Lik: -11276.106, Max-Change: 0.00022Iteration: 63, Log-Lik: -11276.106, Max-Change: 0.00016Iteration: 64, Log-Lik: -11276.106, Max-Change: 0.00015Iteration: 65, Log-Lik: -11276.106, Max-Change: 0.00013Iteration: 66, Log-Lik: -11276.106, Max-Change: 0.00011Iteration: 67, Log-Lik: -11276.106, Max-Change: 0.00026Iteration: 68, Log-Lik: -11276.105, Max-Change: 0.00013Iteration: 69, Log-Lik: -11276.105, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_3, model = 1, group = group_3, 
    invariance = c("slopes", "intercepts", "free_var"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 69 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -11276.11
Estimated parameters: 50 
AIC = 22588.21
BIC = 22692.37; SABIC = 22635.18
G2 (747) = 1525.09, p = 0
RMSEA = 0.021, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.561 0.314
Obedient     0.718 0.516
GoodMannered 0.700 0.490
WellBehaved  0.624 0.390
Polite       0.683 0.467
Orderly      0.738 0.545
Disciplined  0.734 0.538
Loyal        0.591 0.349

SS loadings:  3.609 
Proportion Var:  0.451 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.568 0.323
Obedient     0.725 0.526
GoodMannered 0.707 0.500
WellBehaved  0.632 0.399
Polite       0.690 0.477
Orderly      0.745 0.555
Disciplined  0.740 0.548
Loyal        0.599 0.358

SS loadings:  3.685 
Proportion Var:  0.461 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.528 0.279
Obedient     0.688 0.473
GoodMannered 0.669 0.447
WellBehaved  0.592 0.350
Polite       0.652 0.425
Orderly      0.709 0.502
Disciplined  0.704 0.496
Loyal        0.558 0.312

SS loadings:  3.284 
Proportion Var:  0.411 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control SRMSR.Girl       TLI
stats 754.6494 90 0 0.05539072 0.05176497 0.05906462 0.06621083    0.05789625 0.06321922 0.8706654
            CFI
stats 0.8614272
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar1_3    22588.21 22635.18 22626.10 22692.37 -11276.10             
mod_configural_3 22244.41 22369.66 22345.44 22522.17 -11074.21 403.797 30 0
Iteration: 1, Log-Lik: -11538.508, Max-Change: 0.38633Iteration: 2, Log-Lik: -11349.356, Max-Change: 0.24015Iteration: 3, Log-Lik: -11299.591, Max-Change: 0.14340Iteration: 4, Log-Lik: -11284.755, Max-Change: 0.08483Iteration: 5, Log-Lik: -11280.168, Max-Change: 0.04979Iteration: 6, Log-Lik: -11278.670, Max-Change: 0.02996Iteration: 7, Log-Lik: -11278.042, Max-Change: 0.01326Iteration: 8, Log-Lik: -11277.952, Max-Change: 0.00756Iteration: 9, Log-Lik: -11277.920, Max-Change: 0.00474Iteration: 10, Log-Lik: -11277.905, Max-Change: 0.00177Iteration: 11, Log-Lik: -11277.903, Max-Change: 0.00102Iteration: 12, Log-Lik: -11277.902, Max-Change: 0.00058Iteration: 13, Log-Lik: -11277.902, Max-Change: 0.00020Iteration: 14, Log-Lik: -11277.902, Max-Change: 0.00011Iteration: 15, Log-Lik: -11277.902, Max-Change: 0.00010

Call:
multipleGroup(data = irt_data_3, model = 1, group = group_3, 
    invariance = c("slopes", "intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 15 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -11277.9
Estimated parameters: 48 
AIC = 22587.8
BIC = 22680.39; SABIC = 22629.55
G2 (749) = 1528.68, p = 0
RMSEA = 0.021, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.551 0.303
Obedient     0.709 0.502
GoodMannered 0.693 0.480
WellBehaved  0.616 0.379
Polite       0.675 0.456
Orderly      0.732 0.535
Disciplined  0.727 0.528
Loyal        0.583 0.340

SS loadings:  3.524 
Proportion Var:  0.441 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.551 0.303
Obedient     0.709 0.502
GoodMannered 0.693 0.480
WellBehaved  0.616 0.379
Polite       0.675 0.456
Orderly      0.732 0.535
Disciplined  0.727 0.528
Loyal        0.583 0.340

SS loadings:  3.524 
Proportion Var:  0.441 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.551 0.303
Obedient     0.709 0.502
GoodMannered 0.693 0.480
WellBehaved  0.616 0.379
Polite       0.675 0.456
Orderly      0.732 0.535
Disciplined  0.727 0.528
Loyal        0.583 0.340

SS loadings:  3.524 
Proportion Var:  0.441 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control SRMSR.Girl      TLI
stats 753.6604 92 0 0.05466201 0.05107275 0.05829877 0.06384998    0.05781632  0.0693114 0.874046
            CFI
stats 0.8620504
                         AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_fullconstrain_3 22587.80 22629.55 22621.48 22680.39 -11277.90            
mod_configural_3    22244.41 22369.66 22345.44 22522.17 -11074.21 407.39 32 0
                      AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_metric_3     22235.08 22318.57 22302.43 22420.24 -11085.54                
mod_configural_3 22244.41 22369.66 22345.44 22522.17 -11074.21 22.662 16 0.123
                   AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar2_3 22487.35 22539.54 22529.45 22603.08 -11223.68             
mod_metric_3  22235.08 22318.57 22302.43 22420.24 -11085.54 276.279 12 0
                   AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar1_3 22588.21 22635.18 22626.10 22692.37 -11276.10             
mod_scalar2_3 22487.35 22539.54 22529.45 22603.08 -11223.68 104.857  2 0
                         AIC    SABIC       HQ      BIC   logLik    X2 df     p
mod_fullconstrain_3 22587.80 22629.55 22621.48 22680.39 -11277.9               
mod_scalar1_3       22588.21 22635.18 22626.10 22692.37 -11276.1 3.592  2 0.166
                         AIC    SABIC       HQ      BIC    logLik      X2 df     p
mod_fullconstrain_3 22587.80 22629.55 22621.48 22680.39 -11277.90                 
mod_scalar1_3       22588.21 22635.18 22626.10 22692.37 -11276.10   3.592  2 0.166
mod_scalar2_3       22487.35 22539.54 22529.45 22603.08 -11223.68 104.857  2     0
mod_metric_3        22235.08 22318.57 22302.43 22420.24 -11085.54 276.279 12     0
mod_configural_3    22244.41 22369.66 22345.44 22522.17 -11074.21  22.662 16 0.123

----------
GROUP: Boy 
                F1    h2
Respect      0.565 0.319
Obedient     0.717 0.515
GoodMannered 0.679 0.461
WellBehaved  0.610 0.372
Polite       0.674 0.454
Orderly      0.709 0.503
Disciplined  0.728 0.529
Loyal        0.563 0.317

SS loadings:  3.47 
Proportion Var:  0.434 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.584 0.341
Obedient     0.734 0.539
GoodMannered 0.697 0.486
WellBehaved  0.629 0.395
Polite       0.692 0.479
Orderly      0.727 0.528
Disciplined  0.744 0.554
Loyal        0.582 0.339

SS loadings:  3.661 
Proportion Var:  0.458 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.528 0.279
Obedient     0.683 0.467
GoodMannered 0.643 0.414
WellBehaved  0.573 0.328
Polite       0.638 0.407
Orderly      0.675 0.455
Disciplined  0.694 0.481
Loyal        0.526 0.277

SS loadings:  3.108 
Proportion Var:  0.389 

Factor correlations: 

   F1
F1  1
$Boy
$items
                a1      d g u
Respect      1.165 -0.201 0 1
Obedient     1.752 -1.597 0 1
GoodMannered 1.574  0.115 0 1
WellBehaved  1.310 -1.362 0 1
Polite       1.553  0.629 0 1
Orderly      1.713 -1.737 0 1
Disciplined  1.805 -1.193 0 1
Loyal        1.159 -0.564 0 1

$means
F1 
 0 

$cov
   F1
F1  1


$Control
$items
                a1      d g u
Respect      1.165 -0.201 0 1
Obedient     1.752 -1.597 0 1
GoodMannered 1.574  0.115 0 1
WellBehaved  1.310 -1.362 0 1
Polite       1.553  0.629 0 1
Orderly      1.713 -1.737 0 1
Disciplined  1.805 -1.193 0 1
Loyal        1.159 -0.564 0 1

$means
   F1 
0.425 

$cov
      F1
F1 1.104


$Girl
$items
                a1      d g u
Respect      1.165 -0.201 0 1
Obedient     1.752 -1.597 0 1
GoodMannered 1.574  0.115 0 1
WellBehaved  1.310 -1.362 0 1
Polite       1.553  0.629 0 1
Orderly      1.713 -1.737 0 1
Disciplined  1.805 -1.193 0 1
Loyal        1.159 -0.564 0 1

$means
   F1 
0.572 

$cov
      F1
F1 0.825


$Boy
             Respect Obedient GoodMannered WellBehaved Polite Orderly Disciplined  Loyal
Respect           NA   -0.200        0.247      -0.196  0.191  -0.297      -0.204 -0.289
Obedient      32.337       NA       -0.226       0.153 -0.129  -0.260       0.131  0.236
GoodMannered  49.072   41.072           NA       0.118  0.135  -0.194      -0.163  0.192
WellBehaved   30.965   18.952       11.203          NA -0.130   0.175      -0.059 -0.169
Polite        29.430   13.337       14.664      13.676     NA  -0.186      -0.059  0.197
Orderly       71.053   54.304       30.304      24.571 27.820      NA      -0.200  0.228
Disciplined   33.478   13.800       21.299       2.810  2.791  32.285          NA  0.185
Loyal         67.192   45.011       29.778      22.943 31.346  41.840      27.646     NA

$Control
             Respect Obedient GoodMannered WellBehaved Polite Orderly Disciplined  Loyal
Respect           NA    0.217        0.222       0.107  0.108  -0.213      -0.111 -0.211
Obedient      37.643       NA       -0.324      -0.200 -0.203   0.298      -0.207 -0.291
GoodMannered  39.404   83.604           NA       0.175  0.149   0.159      -0.144 -0.172
WellBehaved    9.165   32.070       24.507          NA -0.091  -0.165      -0.089 -0.223
Polite         9.250   33.008       17.700       6.674     NA  -0.156       0.044  0.163
Orderly       36.319   70.855       20.142      21.618 19.504      NA       0.152  0.195
Disciplined    9.879   34.340       16.548       6.361  1.539  18.346          NA  0.155
Loyal         35.457   67.537       23.500      39.713 21.290  30.427      19.216     NA

$Girl
             Respect Obedient GoodMannered WellBehaved Polite Orderly Disciplined  Loyal
Respect           NA   -0.146        0.129      -0.138  0.095  -0.149      -0.121 -0.138
Obedient      17.144       NA       -0.118       0.111 -0.117   0.081       0.114 -0.075
GoodMannered  13.381   11.218           NA       0.097  0.082   0.044      -0.053  0.059
WellBehaved   15.385    9.947        7.573          NA -0.082   0.067       0.066  0.049
Polite         7.295   10.958        5.437       5.433     NA   0.030      -0.062 -0.022
Orderly       17.959    5.284        1.573       3.634  0.728      NA       0.089  0.095
Disciplined   11.706   10.529        2.263       3.484  3.063   6.384          NA  0.065
Loyal         15.431    4.501        2.771       1.905  0.400   7.293       3.425     NA

group_1b
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               SIDS  UIDS   SIDN  UIDN   ESSD theta.of.max.D  max.D mean.ES.foc mean.ES.ref
Independence -0.054 0.066 -0.044 0.065 -0.254          0.284 -0.099       0.637       0.691
Obedience     0.048 0.048  0.043 0.044  0.211         -0.291  0.071       0.575       0.527
Curiosity     0.114 0.138  0.115 0.137  0.501         -1.259  0.333       0.746       0.632
Considerate  -0.043 0.059 -0.044 0.062 -0.265          1.089 -0.136       0.349       0.392
          Effect Size       Value
1                STDS  0.06513265
2                UTDS  0.31079584
3              UETSDS  0.22666002
4               ETSSD  0.16975860
5         Starks.DTFR  0.06929557
6               UDTFR  0.30759583
7              UETSDN  0.23674292
8 theta.of.max.test.D -1.25912040
9           Test.Dmax  0.47671175
group_1g
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               SIDS  UIDS   SIDN  UIDN   ESSD theta.of.max.D  max.D mean.ES.foc mean.ES.ref
Independence -0.358 0.358 -0.335 0.335 -2.020          0.294 -0.428       0.274       0.632
Obedience     0.284 0.284  0.287 0.288  1.550          1.580  0.588       0.864       0.580
Curiosity    -0.264 0.264 -0.239 0.240 -1.128         -0.812 -0.425       0.479       0.743
Considerate  -0.123 0.136 -0.106 0.131 -0.695         -0.261 -0.186       0.226       0.349
          Effect Size      Value
1                STDS -0.4616370
2                UTDS  1.0419471
3              UETSDS  0.5254434
4               ETSSD -1.0234808
5         Starks.DTFR -0.3924419
6               UDTFR  0.9938971
7              UETSDN  0.5123477
8 theta.of.max.test.D -0.8120577
9           Test.Dmax -0.8059052

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               SIDS  UIDS   SIDN  UIDN   ESSD theta.of.max.D  max.D mean.ES.foc mean.ES.ref
Independence  0.089 0.106  0.078 0.099  0.438          0.278  0.145       0.733       0.645
Obedience    -0.123 0.131 -0.111 0.119 -0.537          0.578 -0.210       0.472       0.594
Curiosity    -0.020 0.099 -0.012 0.090 -0.062         -0.981  0.172       0.658       0.678
Considerate   0.072 0.072  0.068 0.068  0.474          0.578  0.084       0.374       0.302
          Effect Size       Value
1                STDS  0.01798839
2                UTDS  0.40762863
3              UETSDS  0.09777557
4               ETSSD  0.03949922
5         Starks.DTFR  0.02299091
6               UDTFR  0.37582814
7              UETSDN  0.09423365
8 theta.of.max.test.D -0.72256225
9           Test.Dmax  0.23671154
group_2g
  2   3 
562 562 
Iteration: 1, Log-Lik: -3001.496, Max-Change: 0.93127Iteration: 2, Log-Lik: -2678.843, Max-Change: 0.52779Iteration: 3, Log-Lik: -2614.928, Max-Change: 0.41104Iteration: 4, Log-Lik: -2582.208, Max-Change: 0.29562Iteration: 5, Log-Lik: -2568.727, Max-Change: 0.19959Iteration: 6, Log-Lik: -2563.599, Max-Change: 0.13149Iteration: 7, Log-Lik: -2561.623, Max-Change: 0.08189Iteration: 8, Log-Lik: -2560.800, Max-Change: 0.06869Iteration: 9, Log-Lik: -2560.443, Max-Change: 0.05657Iteration: 10, Log-Lik: -2560.135, Max-Change: 0.03800Iteration: 11, Log-Lik: -2560.089, Max-Change: 0.03378Iteration: 12, Log-Lik: -2560.055, Max-Change: 0.03032Iteration: 13, Log-Lik: -2559.942, Max-Change: 0.01349Iteration: 14, Log-Lik: -2559.936, Max-Change: 0.01224Iteration: 15, Log-Lik: -2559.932, Max-Change: 0.01128Iteration: 16, Log-Lik: -2559.917, Max-Change: 0.00624Iteration: 17, Log-Lik: -2559.915, Max-Change: 0.00643Iteration: 18, Log-Lik: -2559.914, Max-Change: 0.00563Iteration: 19, Log-Lik: -2559.913, Max-Change: 0.00522Iteration: 20, Log-Lik: -2559.912, Max-Change: 0.00445Iteration: 21, Log-Lik: -2559.912, Max-Change: 0.00468Iteration: 22, Log-Lik: -2559.910, Max-Change: 0.00160Iteration: 23, Log-Lik: -2559.910, Max-Change: 0.00035Iteration: 24, Log-Lik: -2559.910, Max-Change: 0.00175Iteration: 25, Log-Lik: -2559.910, Max-Change: 0.00164Iteration: 26, Log-Lik: -2559.910, Max-Change: 0.00031Iteration: 27, Log-Lik: -2559.910, Max-Change: 0.00153Iteration: 28, Log-Lik: -2559.910, Max-Change: 0.00029Iteration: 29, Log-Lik: -2559.910, Max-Change: 0.00141Iteration: 30, Log-Lik: -2559.910, Max-Change: 0.00131Iteration: 31, Log-Lik: -2559.910, Max-Change: 0.00135Iteration: 32, Log-Lik: -2559.909, Max-Change: 0.00023Iteration: 33, Log-Lik: -2559.909, Max-Change: 0.00112Iteration: 34, Log-Lik: -2559.909, Max-Change: 0.00021Iteration: 35, Log-Lik: -2559.909, Max-Change: 0.00102Iteration: 36, Log-Lik: -2559.909, Max-Change: 0.00095Iteration: 37, Log-Lik: -2559.909, Max-Change: 0.00030Iteration: 38, Log-Lik: -2559.909, Max-Change: 0.00086Iteration: 39, Log-Lik: -2559.909, Max-Change: 0.00017Iteration: 40, Log-Lik: -2559.909, Max-Change: 0.00016Iteration: 41, Log-Lik: -2559.909, Max-Change: 0.00078Iteration: 42, Log-Lik: -2559.909, Max-Change: 0.00015Iteration: 43, Log-Lik: -2559.909, Max-Change: 0.00075Iteration: 44, Log-Lik: -2559.909, Max-Change: 0.00018Iteration: 45, Log-Lik: -2559.909, Max-Change: 0.00068Iteration: 46, Log-Lik: -2559.909, Max-Change: 0.00017Iteration: 47, Log-Lik: -2559.909, Max-Change: 0.00063Iteration: 48, Log-Lik: -2559.909, Max-Change: 0.00011Iteration: 49, Log-Lik: -2559.909, Max-Change: 0.00011Iteration: 50, Log-Lik: -2559.909, Max-Change: 0.00057Iteration: 51, Log-Lik: -2559.909, Max-Change: 0.00011Iteration: 52, Log-Lik: -2559.909, Max-Change: 0.00011Iteration: 53, Log-Lik: -2559.909, Max-Change: 0.00054Iteration: 54, Log-Lik: -2559.909, Max-Change: 0.00049Iteration: 55, Log-Lik: -2559.909, Max-Change: 0.00013Iteration: 56, Log-Lik: -2559.909, Max-Change: 0.00047Iteration: 57, Log-Lik: -2559.909, Max-Change: 0.00009
               SIDS  UIDS   SIDN  UIDN   ESSD theta.of.max.D  max.D mean.ES.foc mean.ES.ref
Independence -0.444 0.444 -0.411 0.411 -2.180          0.060 -0.558       0.286       0.730
Obedience     0.381 0.381  0.370 0.370  1.910          1.054  0.607       0.863       0.483
Curiosity    -0.108 0.108 -0.098 0.099 -0.346         -0.462 -0.211       0.538       0.646
Considerate  -0.147 0.147 -0.139 0.139 -0.962          0.471 -0.176       0.224       0.371
          Effect Size      Value
1                STDS -0.3176218
2                UTDS  1.0794178
3              UETSDS  0.3501464
4               ETSSD -0.6422644
5         Starks.DTFR -0.2778066
6               UDTFR  1.0182174
7              UETSDN  0.3350578
8 theta.of.max.test.D -0.4621251
9           Test.Dmax -0.5885789
group_3b
  1   3 
798 805 
Iteration: 1, Log-Lik: -7637.011, Max-Change: 0.81415Iteration: 2, Log-Lik: -7369.032, Max-Change: 0.27846Iteration: 3, Log-Lik: -7326.440, Max-Change: 0.17147Iteration: 4, Log-Lik: -7313.223, Max-Change: 0.09885Iteration: 5, Log-Lik: -7308.562, Max-Change: 0.05666Iteration: 6, Log-Lik: -7306.759, Max-Change: 0.03316Iteration: 7, Log-Lik: -7305.665, Max-Change: 0.01462Iteration: 8, Log-Lik: -7305.492, Max-Change: 0.00941Iteration: 9, Log-Lik: -7305.399, Max-Change: 0.00708Iteration: 10, Log-Lik: -7305.301, Max-Change: 0.00365Iteration: 11, Log-Lik: -7305.292, Max-Change: 0.00278Iteration: 12, Log-Lik: -7305.287, Max-Change: 0.00212Iteration: 13, Log-Lik: -7305.279, Max-Change: 0.00081Iteration: 14, Log-Lik: -7305.279, Max-Change: 0.00068Iteration: 15, Log-Lik: -7305.278, Max-Change: 0.00042Iteration: 16, Log-Lik: -7305.278, Max-Change: 0.00033Iteration: 17, Log-Lik: -7305.278, Max-Change: 0.00019Iteration: 18, Log-Lik: -7305.278, Max-Change: 0.00017Iteration: 19, Log-Lik: -7305.278, Max-Change: 0.00010

Call:
multipleGroup(data = irt_data_3b, model = 1, group = group_3b)

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 19 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -7305.278
Estimated parameters: 32 
AIC = 14674.56
BIC = 14846.7; SABIC = 14745.05
G2 (478) = 777.21, p = 0
RMSEA = 0.02, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
                F1    h2
Respect      0.536 0.287
Obedient     0.732 0.535
GoodMannered 0.664 0.441
WellBehaved  0.602 0.363
Polite       0.701 0.492
Orderly      0.740 0.547
Disciplined  0.679 0.461
Loyal        0.659 0.434

SS loadings:  3.56 
Proportion Var:  0.445 

Factor correlations: 

   F1
F1  1

----------
GROUP: Control 
                F1    h2
Respect      0.625 0.391
Obedient     0.732 0.535
GoodMannered 0.730 0.532
WellBehaved  0.596 0.355
Polite       0.677 0.459
Orderly      0.755 0.570
Disciplined  0.732 0.535
Loyal        0.575 0.331

SS loadings:  3.708 
Proportion Var:  0.464 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Control       TLI       CFI
stats 224.2193 40 0 0.05361747 0.04687906 0.06054509 0.05514791    0.05392828 0.9236735 0.9454811
               SIDS  UIDS   SIDN  UIDN   ESSD theta.of.max.D  max.D mean.ES.foc mean.ES.ref
Respect       0.244 0.244  0.234 0.234  1.153          0.337  0.310       0.610       0.366
Obedient      0.274 0.274  0.260 0.260  1.128          0.640  0.434       0.461       0.187
GoodMannered -0.003 0.027 -0.004 0.028 -0.011         -1.067 -0.050       0.573       0.576
WellBehaved   0.123 0.123  0.119 0.119  0.625          0.752  0.174       0.375       0.251
Polite        0.141 0.141  0.137 0.137  0.575         -0.680  0.227       0.735       0.594
Orderly      -0.027 0.027 -0.026 0.026 -0.110          0.464 -0.044       0.269       0.296
Disciplined   0.159 0.159  0.151 0.151  0.630          0.640  0.257       0.442       0.282
Loyal        -0.052 0.057 -0.050 0.054 -0.225          0.879 -0.104       0.413       0.465
          Effect Size     Value
1                STDS 0.8592377
2                UTDS 1.0538688
3              UETSDS 0.8592377
4               ETSSD 0.4627106
5         Starks.DTFR 0.8211085
6               UDTFR 1.0085220
7              UETSDN 0.8211085
8 theta.of.max.test.D 0.4824288
9           Test.Dmax 1.1491830
group_3g
  1   2 
798 805 
Iteration: 1, Log-Lik: -7691.323, Max-Change: 0.43380Iteration: 2, Log-Lik: -7521.421, Max-Change: 0.27758Iteration: 3, Log-Lik: -7485.697, Max-Change: 0.17823Iteration: 4, Log-Lik: -7475.050, Max-Change: 0.11222Iteration: 5, Log-Lik: -7471.562, Max-Change: 0.07492Iteration: 6, Log-Lik: -7470.332, Max-Change: 0.04304Iteration: 7, Log-Lik: -7469.794, Max-Change: 0.02034Iteration: 8, Log-Lik: -7469.702, Max-Change: 0.01299Iteration: 9, Log-Lik: -7469.665, Max-Change: 0.00782Iteration: 10, Log-Lik: -7469.643, Max-Change: 0.00284Iteration: 11, Log-Lik: -7469.640, Max-Change: 0.00181Iteration: 12, Log-Lik: -7469.638, Max-Change: 0.00122Iteration: 13, Log-Lik: -7469.636, Max-Change: 0.00065Iteration: 14, Log-Lik: -7469.636, Max-Change: 0.00035Iteration: 15, Log-Lik: -7469.636, Max-Change: 0.00032Iteration: 16, Log-Lik: -7469.636, Max-Change: 0.00022Iteration: 17, Log-Lik: -7469.636, Max-Change: 0.00019Iteration: 18, Log-Lik: -7469.636, Max-Change: 0.00015Iteration: 19, Log-Lik: -7469.636, Max-Change: 0.00008

Call:
multipleGroup(data = irt_data_3g, model = 1, group = group_3g)

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 19 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -7469.636
Estimated parameters: 32 
AIC = 15003.27
BIC = 15175.42; SABIC = 15073.76
G2 (478) = 730.57, p = 0
RMSEA = 0.018, CFI = NaN, TLI = NaN

----------
GROUP: Control 
                F1    h2
Respect      0.625 0.391
Obedient     0.732 0.535
GoodMannered 0.730 0.532
WellBehaved  0.596 0.355
Polite       0.677 0.459
Orderly      0.755 0.570
Disciplined  0.732 0.535
Loyal        0.575 0.331

SS loadings:  3.708 
Proportion Var:  0.464 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
                F1    h2
Respect      0.399 0.159
Obedient     0.673 0.453
GoodMannered 0.684 0.468
WellBehaved  0.625 0.390
Polite       0.582 0.339
Orderly      0.768 0.590
Disciplined  0.718 0.516
Loyal        0.582 0.339

SS loadings:  3.253 
Proportion Var:  0.407 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Control SRMSR.Girl       TLI       CFI
stats 213.7886 40 0 0.05207741 0.04532071 0.05902651    0.05392224 0.05365486 0.9247709 0.9462649
               SIDS  UIDS   SIDN  UIDN   ESSD theta.of.max.D  max.D mean.ES.foc mean.ES.ref
Respect       0.031 0.080  0.033 0.084  0.163         -1.640  0.198       0.641       0.610
Obedient     -0.103 0.103 -0.098 0.098 -0.394          0.595 -0.170       0.355       0.459
GoodMannered  0.128 0.128  0.122 0.122  0.500         -0.624  0.209       0.699       0.571
WellBehaved  -0.019 0.019 -0.018 0.019 -0.086         -0.285 -0.028       0.355       0.373
Polite        0.031 0.040  0.032 0.042  0.161         -1.640  0.133       0.768       0.737
Orderly       0.088 0.088  0.084 0.084  0.331          0.692  0.160       0.355       0.267
Disciplined   0.050 0.050  0.047 0.047  0.180         -0.026  0.074       0.489       0.439
Loyal         0.104 0.104  0.100 0.100  0.486          0.208  0.130       0.516       0.411
          Effect Size      Value
1                STDS  0.3099954
2                UTDS  0.6136435
3              UETSDS  0.3106622
4               ETSSD  0.1688614
5         Starks.DTFR  0.3021560
6               UDTFR  0.5953083
7              UETSDN  0.3052906
8 theta.of.max.test.D -1.0634974
9           Test.Dmax  0.5458587
Analysis of Variance Table

Response: Therm_Trump
                            Df  Sum Sq Mean Sq F value    Pr(>F)    
auth                         1  102123  102123 66.3200 6.155e-16 ***
as.factor(treatment)         2   16958    8479  5.5065  0.004113 ** 
auth:as.factor(treatment)    2    3329    1665  1.0811  0.339391    
Residuals                 2348 3615573    1540                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.798   3.798   3.798   3.798   3.798   3.798 
 treatment auth.trend    SE   df lower.CL upper.CL
 1               3.80 0.599 2348     2.62     4.97
 2               2.64 0.631 2348     1.40     3.88
 3               2.77 0.632 2348     1.53     4.01

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2     1.16 0.870 2348   1.331  0.3781
 treatment1 - treatment3     1.03 0.870 2348   1.181  0.4647
 treatment2 - treatment3    -0.13 0.893 2348  -0.145  0.9885

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: Therm_Biden
                            Df  Sum Sq Mean Sq F value   Pr(>F)    
auth                         1   37719   37719 26.1141 3.48e-07 ***
as.factor(treatment)         2    1464     732  0.5067   0.6025    
auth:as.factor(treatment)    2      63      31  0.0218   0.9785    
Residuals                 2331 3366858    1444                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend    SE   df lower.CL upper.CL
 1              -1.75 0.586 2331    -2.90   -0.604
 2              -1.92 0.614 2331    -3.12   -0.711
 3              -1.77 0.614 2331    -2.97   -0.563

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2   0.1621 0.849 2331   0.191  0.9801
 treatment1 - treatment3   0.0144 0.849 2331   0.017  0.9998
 treatment2 - treatment3  -0.1477 0.869 2331  -0.170  0.9842

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: Therm_Dem
                            Df  Sum Sq Mean Sq F value    Pr(>F)    
auth                         1   29310 29310.5 22.8289 1.882e-06 ***
as.factor(treatment)         2    1754   877.1  0.6832    0.5051    
auth:as.factor(treatment)    2     862   430.9  0.3356    0.7149    
Residuals                 2317 2974840  1283.9                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend    SE   df lower.CL upper.CL
 1              -1.73 0.556 2317    -2.82  -0.6440
 2              -1.77 0.580 2317    -2.91  -0.6344
 3              -1.18 0.579 2317    -2.31  -0.0396

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2   0.0383 0.804 2317   0.048  0.9987
 treatment1 - treatment3  -0.5582 0.803 2317  -0.695  0.7663
 treatment2 - treatment3  -0.5965 0.820 2317  -0.727  0.7472

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: Therm_GOP
                            Df  Sum Sq Mean Sq F value    Pr(>F)    
auth                         1   63601   63601 55.7463 1.161e-13 ***
as.factor(treatment)         2    9388    4694  4.1143   0.01646 *  
auth:as.factor(treatment)    2    3416    1708  1.4972   0.22396    
Residuals                 2316 2642310    1141                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend    SE   df lower.CL upper.CL
 1               3.04 0.521 2316    2.021     4.06
 2               2.48 0.550 2316    1.401     3.56
 3               1.74 0.544 2316    0.674     2.81

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2    0.563 0.757 2316   0.744  0.7372
 treatment1 - treatment3    1.301 0.753 2316   1.728  0.1949
 treatment2 - treatment3    0.738 0.773 2316   0.954  0.6058

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: Therm_BLM
                            Df  Sum Sq Mean Sq F value    Pr(>F)    
auth                         1   23384 23384.1 17.7536 2.612e-05 ***
as.factor(treatment)         2     482   241.1  0.1831    0.8327    
auth:as.factor(treatment)    2    1755   877.3  0.6661    0.5138    
Residuals                 2287 3012307  1317.1                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2   -0.631 0.820 2287  -0.770  0.7215
 treatment1 - treatment3   -0.922 0.821 2287  -1.124  0.4993
 treatment2 - treatment3   -0.291 0.836 2287  -0.348  0.9354

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: Therm_Cops
                            Df  Sum Sq Mean Sq F value   Pr(>F)   
auth                         1    8724  8724.3  7.8135 0.005229 **
as.factor(treatment)         2    1483   741.5  0.6641 0.514847   
auth:as.factor(treatment)    2     144    71.8  0.0643 0.937740   
Residuals                 2300 2568111  1116.6                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = Therm_Cops ~ auth * as.factor(treatment), data = CFAdata_3)

Residuals:
    Min      1Q  Median      3Q     Max 
-60.974 -26.138  -0.228  31.547  47.554 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                52.44569    2.34326  22.382   <2e-16 ***
auth                        0.86157    0.52000   1.657   0.0977 .  
as.factor(treatment)2       1.55231    3.46932   0.447   0.6546    
as.factor(treatment)3       0.57564    3.11468   0.185   0.8534    
auth:as.factor(treatment)2  0.01047    0.75136   0.014   0.9889    
auth:as.factor(treatment)3 -0.23116    0.75101  -0.308   0.7583    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 33.42 on 2300 degrees of freedom
  (102 observations deleted due to missingness)
Multiple R-squared:  0.004014,	Adjusted R-squared:  0.001849 
F-statistic: 1.854 on 5 and 2300 DF,  p-value: 0.09922

 treatment auth.trend    SE   df lower.CL upper.CL
 1              0.862 0.520 2300   -0.158     1.88
 2              0.872 0.542 2300   -0.192     1.94
 3              0.630 0.542 2300   -0.432     1.69

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2  -0.0105 0.751 2300  -0.014  0.9999
 treatment1 - treatment3   0.2312 0.751 2300   0.308  0.9491
 treatment2 - treatment3   0.2416 0.767 2300   0.315  0.9467

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: Immigration
                            Df  Sum Sq Mean Sq  F value    Pr(>F)    
auth                         1  258.78 258.784 337.6807 < 2.2e-16 ***
as.factor(treatment)         2   32.40  16.199  21.1376 7.944e-10 ***
auth:as.factor(treatment)    2    3.39   1.694   2.2098    0.1099    
Residuals                 2397 1836.96   0.766                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend     SE   df lower.CL upper.CL
 1              0.171 0.0132 2397    0.145    0.197
 2              0.131 0.0140 2397    0.103    0.158
 3              0.155 0.0140 2397    0.127    0.182

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2   0.0403 0.0192 2397   2.096  0.0908
 treatment1 - treatment3   0.0163 0.0193 2397   0.848  0.6732
 treatment2 - treatment3  -0.0240 0.0198 2397  -1.212  0.4460

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: GR
                            Df  Sum Sq Mean Sq  F value    Pr(>F)    
auth                         1  230.63 230.631 244.4258 < 2.2e-16 ***
as.factor(treatment)         2   15.65   7.825   8.2933 0.0002575 ***
auth:as.factor(treatment)    2    4.19   2.093   2.2180 0.1090494    
Residuals                 2392 2257.00   0.944                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend     SE   df lower.CL upper.CL
 1              0.163 0.0147 2392   0.1341    0.192
 2              0.118 0.0155 2392   0.0875    0.148
 3              0.141 0.0156 2392   0.1106    0.172

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2   0.0450 0.0214 2392   2.106  0.0888
 treatment1 - treatment3   0.0218 0.0214 2392   1.016  0.5668
 treatment2 - treatment3  -0.0232 0.0220 2392  -1.058  0.5402

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: DP
                            Df  Sum Sq Mean Sq  F value    Pr(>F)    
auth                         1  130.57 130.566 125.1573 < 2.2e-16 ***
as.factor(treatment)         2   14.81   7.403   7.0968 0.0008453 ***
auth:as.factor(treatment)    2    6.55   3.277   3.1408 0.0434260 *  
Residuals                 2393 2496.42   1.043                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend     SE   df lower.CL upper.CL
 1             0.1379 0.0154 2393   0.1076    0.168
 2             0.0992 0.0163 2393   0.0672    0.131
 3             0.0836 0.0163 2393   0.0516    0.116

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2   0.0387 0.0224 2393   1.725  0.1959
 treatment1 - treatment3   0.0543 0.0225 2393   2.417  0.0415
 treatment2 - treatment3   0.0156 0.0231 2393   0.676  0.7772

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: hostile_sexism
                            Df Sum Sq Mean Sq  F value    Pr(>F)    
auth                         1   9651  9650.5 246.6513 < 2.2e-16 ***
as.factor(treatment)         2   1249   624.3  15.9560 1.308e-07 ***
auth:as.factor(treatment)    2    548   274.2   7.0088 0.0009227 ***
Residuals                 2372  92807    39.1                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = hostile_sexism ~ auth * as.factor(treatment), data = CFAdata_3)

Residuals:
     Min       1Q   Median       3Q      Max 
-16.7617  -4.7104  -0.3455   4.4038  18.6316 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                11.36839    0.42991  26.443   <2e-16 ***
auth                        0.99236    0.09502  10.444   <2e-16 ***
as.factor(treatment)2       1.40490    0.64013   2.195   0.0283 *  
as.factor(treatment)3       1.06383    0.57277   1.857   0.0634 .  
auth:as.factor(treatment)2 -0.34666    0.13809  -2.510   0.0121 *  
auth:as.factor(treatment)3  0.17382    0.13813   1.258   0.2084    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.255 on 2372 degrees of freedom
  (30 observations deleted due to missingness)
Multiple R-squared:  0.1098,	Adjusted R-squared:  0.1079 
F-statistic: 58.52 on 5 and 2372 DF,  p-value: < 2.2e-16

 treatment auth.trend    SE   df lower.CL upper.CL
 1              0.992 0.095 2372    0.806    1.179
 2              0.646 0.100 2372    0.449    0.842
 3              1.166 0.100 2372    0.970    1.363

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2    0.347 0.138 2372   2.510  0.0325
 treatment1 - treatment3   -0.174 0.138 2372  -1.258  0.4190
 treatment2 - treatment3   -0.520 0.142 2372  -3.672  0.0007

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: ben_sexism
                            Df Sum Sq Mean Sq  F value    Pr(>F)    
auth                         1  14101 14101.0 356.6491 < 2.2e-16 ***
as.factor(treatment)         2   1303   651.7  16.4824 7.786e-08 ***
auth:as.factor(treatment)    2     49    24.4   0.6173    0.5395    
Residuals                 2359  93269    39.5                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend    SE   df lower.CL upper.CL
 1               1.19 0.096 2359    0.999     1.38
 2               1.14 0.101 2359    0.943     1.34
 3               1.04 0.101 2359    0.838     1.23

Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2   0.0465 0.140 2359   0.333  0.9407
 treatment1 - treatment3   0.1519 0.139 2359   1.091  0.5197
 treatment2 - treatment3   0.1054 0.143 2359   0.738  0.7411

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: hostil_men
                            Df  Sum Sq Mean Sq F value    Pr(>F)    
auth                         1   376.4  376.38 29.1825 7.249e-08 ***
as.factor(treatment)         2    72.0   36.01  2.7919   0.06151 .  
auth:as.factor(treatment)    2    68.2   34.12  2.6454   0.07119 .  
Residuals                 2354 30360.7   12.90                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend     SE   df lower.CL upper.CL
 1              0.227 0.0548 2354   0.1190    0.334
 2              0.248 0.0580 2354   0.1345    0.362
 3              0.077 0.0575 2354  -0.0358    0.190

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2  -0.0217 0.0798 2354  -0.272  0.9600
 treatment1 - treatment3   0.1496 0.0795 2354   1.882  0.1441
 treatment2 - treatment3   0.1713 0.0817 2354   2.097  0.0907

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: ben_men
                            Df  Sum Sq Mean Sq  F value    Pr(>F)    
auth                         1  2750.9 2750.92 252.7083 < 2.2e-16 ***
as.factor(treatment)         2   426.3  213.17  19.5828 3.674e-09 ***
auth:as.factor(treatment)    2    20.3   10.17   0.9341    0.3931    
Residuals                 2359 25679.5   10.89                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment auth.trend     SE   df lower.CL upper.CL
 1              0.545 0.0504 2359    0.446    0.644
 2              0.446 0.0528 2359    0.342    0.549
 3              0.505 0.0529 2359    0.401    0.609

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2   0.0995 0.0730 2359   1.362  0.3612
 treatment1 - treatment3   0.0401 0.0730 2359   0.549  0.8472
 treatment2 - treatment3  -0.0594 0.0747 2359  -0.795  0.7063

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: auth
                                Df  Sum Sq Mean Sq F value  Pr(>F)    
daughter                         1    14.0  14.028  2.7637 0.09655 .  
as.factor(treatment)             2   537.1 268.564 52.9101 < 2e-16 ***
daughter:as.factor(treatment)    2    15.5   7.730  1.5230 0.21828    
Residuals                     2402 12192.2   5.076                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment daughter.trend    SE   df lower.CL upper.CL
 1                 0.1116 0.162 2402  -0.2054    0.429
 2                 0.0115 0.161 2402  -0.3045    0.328
 3                 0.3935 0.160 2402   0.0796    0.707

Results are averaged over the levels of: daughter 
Confidence level used: 0.95 
 contrast                estimate    SE   df t.ratio p.value
 treatment1 - treatment2    0.100 0.228 2402   0.438  0.8996
 treatment1 - treatment3   -0.282 0.228 2402  -1.239  0.4301
 treatment2 - treatment3   -0.382 0.227 2402  -1.682  0.2124

Results are averaged over the levels of: daughter 
P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: auth
                           Df  Sum Sq Mean Sq F value    Pr(>F)    
pid                         1   247.3 247.327 49.6674 2.371e-12 ***
as.factor(treatment)        2   548.4 274.179 55.0596 < 2.2e-16 ***
pid:as.factor(treatment)    2     4.5   2.245  0.4508    0.6372    
Residuals                2400 11951.2   4.980                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 treatment pid.trend     SE   df lower.CL upper.CL
 1             0.166 0.0339 2400   0.1000    0.233
 2             0.121 0.0343 2400   0.0535    0.188
 3             0.142 0.0348 2400   0.0739    0.210

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2   0.0457 0.0482 2400   0.949  0.6095
 treatment1 - treatment3   0.0244 0.0486 2400   0.501  0.8707
 treatment2 - treatment3  -0.0214 0.0488 2400  -0.438  0.8998

P value adjustment: tukey method for comparing a family of 3 estimates 
Analysis of Variance Table

Response: auth
                                Df  Sum Sq Mean Sq  F value Pr(>F)    
ideology                         1   597.2  597.20 123.6585 <2e-16 ***
as.factor(treatment)             2   553.0  276.50  57.2531 <2e-16 ***
ideology:as.factor(treatment)    2     8.3    4.13   0.8549 0.4255    
Residuals                     2402 11600.4    4.83                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call:
lm(formula = auth ~ ideology * as.factor(treatment), data = CFAdata_3)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9767 -1.7941 -0.0544  1.7297  5.7265 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     2.59618    0.18319  14.172  < 2e-16 ***
ideology                        0.33482    0.04301   7.784 1.03e-14 ***
as.factor(treatment)2           0.49077    0.26916   1.823   0.0684 .  
as.factor(treatment)3          -0.58293    0.27157  -2.147   0.0319 *  
ideology:as.factor(treatment)2 -0.06486    0.06213  -1.044   0.2966    
ideology:as.factor(treatment)3 -0.07453    0.06270  -1.189   0.2347    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.198 on 2402 degrees of freedom
Multiple R-squared:  0.0908,	Adjusted R-squared:  0.0889 
F-statistic: 47.97 on 5 and 2402 DF,  p-value: < 2.2e-16

 treatment ideology.trend     SE   df lower.CL upper.CL
 1                  0.335 0.0430 2402    0.250    0.419
 2                  0.270 0.0448 2402    0.182    0.358
 3                  0.260 0.0456 2402    0.171    0.350

Confidence level used: 0.95 
 contrast                estimate     SE   df t.ratio p.value
 treatment1 - treatment2  0.06486 0.0621 2402   1.044  0.5492
 treatment1 - treatment3  0.07453 0.0627 2402   1.189  0.4600
 treatment2 - treatment3  0.00967 0.0640 2402   0.151  0.9875

P value adjustment: tukey method for comparing a family of 3 estimates 
[1] 0.11157857 0.01153108 0.39352549
[1] 0.11157857 0.01153108 0.39352549
RStudioGD 
        2 
RStudioGD 
        2 
indexed 0B in  0s, 0B/sindexed 1.00TB in  0s, 377.39TB/s                                                                                                 
  0   1 
257 844 
  AfterBG   N   author       sd         se        ci    test
1       1 249 3.935743 2.080024 0.13181616 0.2596219     Boy
2       2 147 3.775510 2.125500 0.17530833 0.3464698    Girl
3       3 619 3.484653 2.427127 0.09755447 0.1915784 Neither
                     Df Sum Sq Mean Sq F value Pr(>F)  
as.factor(AfterBG)    2     39  19.705   3.711 0.0248 *
Residuals          1012   5373   5.309                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = author ~ as.factor(AfterBG), data = coda)

$`as.factor(AfterBG)`
          diff        lwr         upr     p adj
2-1 -0.1602328 -0.7227774  0.40231185 0.7818179
3-1 -0.4510903 -0.8569562 -0.04522438 0.0249849
3-2 -0.2908575 -0.7870819  0.20536686 0.3540280

  AfterBG   N   author       sd         se        ci
1       1 249 3.935743 2.080024 0.13181616 0.2596219
2       2 147 3.775510 2.125500 0.17530833 0.3464698
3       3 619 3.484653 2.427127 0.09755447 0.1915784
RStudioGD 
        2 
group_4
  1   2   3 
249 147 619 
Iteration: 1, Log-Lik: -4778.313, Max-Change: 0.57868Iteration: 2, Log-Lik: -4634.450, Max-Change: 0.38184Iteration: 3, Log-Lik: -4603.079, Max-Change: 0.22119Iteration: 4, Log-Lik: -4593.321, Max-Change: 0.17973Iteration: 5, Log-Lik: -4590.034, Max-Change: 0.14836Iteration: 6, Log-Lik: -4588.821, Max-Change: 0.12902Iteration: 7, Log-Lik: -4588.057, Max-Change: 0.05690Iteration: 8, Log-Lik: -4587.981, Max-Change: 0.04352Iteration: 9, Log-Lik: -4587.946, Max-Change: 0.03569Iteration: 10, Log-Lik: -4587.905, Max-Change: 0.01486Iteration: 11, Log-Lik: -4587.903, Max-Change: 0.00495Iteration: 12, Log-Lik: -4587.903, Max-Change: 0.00196Iteration: 13, Log-Lik: -4587.902, Max-Change: 0.00196Iteration: 14, Log-Lik: -4587.902, Max-Change: 0.00285Iteration: 15, Log-Lik: -4587.902, Max-Change: 0.00040Iteration: 16, Log-Lik: -4587.902, Max-Change: 0.00034Iteration: 17, Log-Lik: -4587.902, Max-Change: 0.00022Iteration: 18, Log-Lik: -4587.902, Max-Change: 0.00022Iteration: 19, Log-Lik: -4587.902, Max-Change: 0.00104Iteration: 20, Log-Lik: -4587.902, Max-Change: 0.00098Iteration: 21, Log-Lik: -4587.902, Max-Change: 0.00019Iteration: 22, Log-Lik: -4587.902, Max-Change: 0.00019Iteration: 23, Log-Lik: -4587.902, Max-Change: 0.00092Iteration: 24, Log-Lik: -4587.902, Max-Change: 0.00087Iteration: 25, Log-Lik: -4587.902, Max-Change: 0.00023Iteration: 26, Log-Lik: -4587.902, Max-Change: 0.00081Iteration: 27, Log-Lik: -4587.902, Max-Change: 0.00016Iteration: 28, Log-Lik: -4587.902, Max-Change: 0.00015Iteration: 29, Log-Lik: -4587.902, Max-Change: 0.00076Iteration: 30, Log-Lik: -4587.901, Max-Change: 0.00014Iteration: 31, Log-Lik: -4587.901, Max-Change: 0.00014Iteration: 32, Log-Lik: -4587.901, Max-Change: 0.00071Iteration: 33, Log-Lik: -4587.901, Max-Change: 0.00068Iteration: 34, Log-Lik: -4587.901, Max-Change: 0.00017Iteration: 35, Log-Lik: -4587.901, Max-Change: 0.00063Iteration: 36, Log-Lik: -4587.901, Max-Change: 0.00013Iteration: 37, Log-Lik: -4587.901, Max-Change: 0.00012Iteration: 38, Log-Lik: -4587.901, Max-Change: 0.00059Iteration: 39, Log-Lik: -4587.901, Max-Change: 0.00011Iteration: 40, Log-Lik: -4587.901, Max-Change: 0.00011Iteration: 41, Log-Lik: -4587.901, Max-Change: 0.00056Iteration: 42, Log-Lik: -4587.901, Max-Change: 0.00053Iteration: 43, Log-Lik: -4587.901, Max-Change: 0.00013Iteration: 44, Log-Lik: -4587.901, Max-Change: 0.00050Iteration: 45, Log-Lik: -4587.901, Max-Change: 0.00011Iteration: 46, Log-Lik: -4587.901, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_4, model = 1, group = group_4)

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 46 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4587.901
Estimated parameters: 48 
AIC = 9271.803
BIC = 9508.09; SABIC = 9355.638
G2 (717) = 689.77, p = 0.7613
RMSEA = 0, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
         F1     h2
auth1 0.206 0.0425
auth2 0.722 0.5220
auth3 0.526 0.2764
auth4 0.419 0.1755
auth5 0.484 0.2347
auth6 0.696 0.4844
auth7 0.688 0.4736
auth8 0.580 0.3359

SS loadings:  2.545 
Proportion Var:  0.318 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
         F1     h2
auth1 0.252 0.0635
auth2 0.597 0.3561
auth3 0.645 0.4158
auth4 0.396 0.1566
auth5 0.390 0.1524
auth6 0.816 0.6657
auth7 0.620 0.3841
auth8 0.768 0.5901

SS loadings:  2.784 
Proportion Var:  0.348 

Factor correlations: 

   F1
F1  1

----------
GROUP: Neither 
         F1    h2
auth1 0.755 0.570
auth2 0.769 0.591
auth3 0.807 0.652
auth4 0.714 0.510
auth5 0.721 0.520
auth6 0.721 0.521
auth7 0.781 0.610
auth8 0.658 0.433

SS loadings:  4.407 
Proportion Var:  0.551 

Factor correlations: 

   F1
F1  1
            M2 df            p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Girl
stats 142.1806 60 1.267373e-08 0.03675277 0.02897441 0.04456401 0.07989586 0.07859816
      SRMSR.Neither       TLI       CFI
stats    0.03363645 0.9518727 0.9656233
Iteration: 1, Log-Lik: -4778.313, Max-Change: 0.45670Iteration: 2, Log-Lik: -4660.163, Max-Change: 0.27691Iteration: 3, Log-Lik: -4634.899, Max-Change: 0.16554Iteration: 4, Log-Lik: -4627.413, Max-Change: 0.08798Iteration: 5, Log-Lik: -4625.054, Max-Change: 0.05125Iteration: 6, Log-Lik: -4624.233, Max-Change: 0.02953Iteration: 7, Log-Lik: -4623.832, Max-Change: 0.01129Iteration: 8, Log-Lik: -4623.766, Max-Change: 0.00842Iteration: 9, Log-Lik: -4623.735, Max-Change: 0.00654Iteration: 10, Log-Lik: -4623.709, Max-Change: 0.00401Iteration: 11, Log-Lik: -4623.705, Max-Change: 0.00257Iteration: 12, Log-Lik: -4623.703, Max-Change: 0.00385Iteration: 13, Log-Lik: -4623.701, Max-Change: 0.00203Iteration: 14, Log-Lik: -4623.700, Max-Change: 0.00043Iteration: 15, Log-Lik: -4623.700, Max-Change: 0.00022Iteration: 16, Log-Lik: -4623.700, Max-Change: 0.00014Iteration: 17, Log-Lik: -4623.700, Max-Change: 0.00013Iteration: 18, Log-Lik: -4623.700, Max-Change: 0.00058Iteration: 19, Log-Lik: -4623.700, Max-Change: 0.00034Iteration: 20, Log-Lik: -4623.700, Max-Change: 0.00010

Call:
multipleGroup(data = irt_data_4, model = 1, group = group_4, 
    invariance = c("slopes"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 20 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4623.7
Estimated parameters: 48 
AIC = 9311.4
BIC = 9468.924; SABIC = 9367.289
G2 (733) = 761.37, p = 0.2269
RMSEA = 0.006, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
         F1    h2
auth1 0.597 0.357
auth2 0.727 0.528
auth3 0.755 0.570
auth4 0.618 0.382
auth5 0.646 0.417
auth6 0.700 0.490
auth7 0.735 0.540
auth8 0.635 0.403

SS loadings:  3.687 
Proportion Var:  0.461 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
         F1    h2
auth1 0.597 0.357
auth2 0.727 0.528
auth3 0.755 0.570
auth4 0.618 0.382
auth5 0.646 0.417
auth6 0.700 0.490
auth7 0.735 0.540
auth8 0.635 0.403

SS loadings:  3.687 
Proportion Var:  0.461 

Factor correlations: 

   F1
F1  1

----------
GROUP: Neither 
         F1    h2
auth1 0.597 0.357
auth2 0.727 0.528
auth3 0.755 0.570
auth4 0.618 0.382
auth5 0.646 0.417
auth6 0.700 0.490
auth7 0.735 0.540
auth8 0.635 0.403

SS loadings:  3.687 
Proportion Var:  0.461 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95 SRMSR.Boy SRMSR.Girl SRMSR.Neither       TLI
stats 228.6754 76 0 0.04451011 0.03793576 0.05116879 0.1401852    0.12958    0.07081248 0.9294123
            CFI
stats 0.9361349
                      AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_metric_4     9311.400 9367.289 9371.227 9468.924 -4623.700            
mod_configural_4 9271.803 9355.638 9361.543 9508.090 -4587.901 71.597 16 0
Iteration: 1, Log-Lik: -4778.313, Max-Change: 0.43693Iteration: 2, Log-Lik: -4661.732, Max-Change: 0.24912Iteration: 3, Log-Lik: -4646.957, Max-Change: 0.11057Iteration: 4, Log-Lik: -4644.566, Max-Change: 0.05437Iteration: 5, Log-Lik: -4643.745, Max-Change: 0.03352Iteration: 6, Log-Lik: -4643.274, Max-Change: 0.02249Iteration: 7, Log-Lik: -4642.647, Max-Change: 0.02692Iteration: 8, Log-Lik: -4642.331, Max-Change: 0.01570Iteration: 9, Log-Lik: -4642.140, Max-Change: 0.01241Iteration: 10, Log-Lik: -4641.801, Max-Change: 0.03746Iteration: 11, Log-Lik: -4641.403, Max-Change: 0.01352Iteration: 12, Log-Lik: -4641.324, Max-Change: 0.00975Iteration: 13, Log-Lik: -4641.186, Max-Change: 0.01518Iteration: 14, Log-Lik: -4641.104, Max-Change: 0.00897Iteration: 15, Log-Lik: -4641.063, Max-Change: 0.00734Iteration: 16, Log-Lik: -4640.997, Max-Change: 0.01671Iteration: 17, Log-Lik: -4640.908, Max-Change: 0.00881Iteration: 18, Log-Lik: -4640.888, Max-Change: 0.00643Iteration: 19, Log-Lik: -4640.859, Max-Change: 0.00843Iteration: 20, Log-Lik: -4640.840, Max-Change: 0.00600Iteration: 21, Log-Lik: -4640.829, Max-Change: 0.00507Iteration: 22, Log-Lik: -4640.814, Max-Change: 0.01133Iteration: 23, Log-Lik: -4640.788, Max-Change: 0.00615Iteration: 24, Log-Lik: -4640.781, Max-Change: 0.00450Iteration: 25, Log-Lik: -4640.773, Max-Change: 0.00557Iteration: 26, Log-Lik: -4640.768, Max-Change: 0.00402Iteration: 27, Log-Lik: -4640.764, Max-Change: 0.00351Iteration: 28, Log-Lik: -4640.759, Max-Change: 0.00824Iteration: 29, Log-Lik: -4640.750, Max-Change: 0.00427Iteration: 30, Log-Lik: -4640.747, Max-Change: 0.00309Iteration: 31, Log-Lik: -4640.744, Max-Change: 0.00367Iteration: 32, Log-Lik: -4640.742, Max-Change: 0.00271Iteration: 33, Log-Lik: -4640.741, Max-Change: 0.00239Iteration: 34, Log-Lik: -4640.739, Max-Change: 0.00577Iteration: 35, Log-Lik: -4640.735, Max-Change: 0.00290Iteration: 36, Log-Lik: -4640.734, Max-Change: 0.00208Iteration: 37, Log-Lik: -4640.733, Max-Change: 0.00241Iteration: 38, Log-Lik: -4640.732, Max-Change: 0.00180Iteration: 39, Log-Lik: -4640.731, Max-Change: 0.00160Iteration: 40, Log-Lik: -4640.730, Max-Change: 0.00389Iteration: 41, Log-Lik: -4640.729, Max-Change: 0.00192Iteration: 42, Log-Lik: -4640.728, Max-Change: 0.00139Iteration: 43, Log-Lik: -4640.728, Max-Change: 0.00162Iteration: 44, Log-Lik: -4640.727, Max-Change: 0.00119Iteration: 45, Log-Lik: -4640.727, Max-Change: 0.00107Iteration: 46, Log-Lik: -4640.726, Max-Change: 0.00262Iteration: 47, Log-Lik: -4640.726, Max-Change: 0.00128Iteration: 48, Log-Lik: -4640.725, Max-Change: 0.00091Iteration: 49, Log-Lik: -4640.725, Max-Change: 0.00105Iteration: 50, Log-Lik: -4640.725, Max-Change: 0.00077Iteration: 51, Log-Lik: -4640.725, Max-Change: 0.00070Iteration: 52, Log-Lik: -4640.724, Max-Change: 0.00175Iteration: 53, Log-Lik: -4640.724, Max-Change: 0.00084Iteration: 54, Log-Lik: -4640.724, Max-Change: 0.00060Iteration: 55, Log-Lik: -4640.724, Max-Change: 0.00069Iteration: 56, Log-Lik: -4640.723, Max-Change: 0.00050Iteration: 57, Log-Lik: -4640.723, Max-Change: 0.00046Iteration: 58, Log-Lik: -4640.723, Max-Change: 0.00115Iteration: 59, Log-Lik: -4640.723, Max-Change: 0.00055Iteration: 60, Log-Lik: -4640.723, Max-Change: 0.00039Iteration: 61, Log-Lik: -4640.723, Max-Change: 0.00047Iteration: 62, Log-Lik: -4640.723, Max-Change: 0.00033Iteration: 63, Log-Lik: -4640.723, Max-Change: 0.00030Iteration: 64, Log-Lik: -4640.723, Max-Change: 0.00076Iteration: 65, Log-Lik: -4640.722, Max-Change: 0.00036Iteration: 66, Log-Lik: -4640.722, Max-Change: 0.00025Iteration: 67, Log-Lik: -4640.722, Max-Change: 0.00030Iteration: 68, Log-Lik: -4640.722, Max-Change: 0.00022Iteration: 69, Log-Lik: -4640.722, Max-Change: 0.00019Iteration: 70, Log-Lik: -4640.722, Max-Change: 0.00050Iteration: 71, Log-Lik: -4640.722, Max-Change: 0.00024Iteration: 72, Log-Lik: -4640.722, Max-Change: 0.00016Iteration: 73, Log-Lik: -4640.722, Max-Change: 0.00018Iteration: 74, Log-Lik: -4640.722, Max-Change: 0.00013Iteration: 75, Log-Lik: -4640.722, Max-Change: 0.00013Iteration: 76, Log-Lik: -4640.722, Max-Change: 0.00027Iteration: 77, Log-Lik: -4640.722, Max-Change: 0.00015Iteration: 78, Log-Lik: -4640.722, Max-Change: 0.00011Iteration: 79, Log-Lik: -4640.722, Max-Change: 0.00014Iteration: 80, Log-Lik: -4640.722, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_4, model = 1, group = group_4, 
    invariance = c("slopes", "intercepts", "free_var", "free_means"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 80 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4640.722
Estimated parameters: 52 
AIC = 9321.444
BIC = 9419.897; SABIC = 9356.375
G2 (745) = 795.41, p = 0.0977
RMSEA = 0.008, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
         F1    h2
auth1 0.493 0.243
auth2 0.641 0.411
auth3 0.643 0.413
auth4 0.528 0.279
auth5 0.512 0.262
auth6 0.595 0.354
auth7 0.648 0.420
auth8 0.536 0.287

SS loadings:  2.669 
Proportion Var:  0.334 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
         F1    h2
auth1 0.508 0.258
auth2 0.656 0.431
auth3 0.658 0.433
auth4 0.544 0.296
auth5 0.527 0.278
auth6 0.611 0.373
auth7 0.663 0.440
auth8 0.551 0.304

SS loadings:  2.811 
Proportion Var:  0.351 

Factor correlations: 

   F1
F1  1

----------
GROUP: Neither 
         F1    h2
auth1 0.641 0.410
auth2 0.776 0.602
auth3 0.778 0.605
auth4 0.676 0.457
auth5 0.660 0.435
auth6 0.737 0.544
auth7 0.782 0.611
auth8 0.683 0.466

SS loadings:  4.13 
Proportion Var:  0.516 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Girl SRMSR.Neither       TLI
stats 284.4465 88 0 0.04692042 0.04086998 0.05304369 0.09404721 0.09464901    0.04866147 0.9215604
            CFI
stats 0.9178251
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar2_4    9321.444 9356.375 9358.836 9419.897 -4640.722             
mod_configural_4 9271.803 9355.638 9361.543 9508.090 -4587.901 105.641 28 0
Iteration: 1, Log-Lik: -4778.313, Max-Change: 0.43694Iteration: 2, Log-Lik: -4663.561, Max-Change: 0.25022Iteration: 3, Log-Lik: -4649.655, Max-Change: 0.10862Iteration: 4, Log-Lik: -4647.782, Max-Change: 0.05140Iteration: 5, Log-Lik: -4647.343, Max-Change: 0.03246Iteration: 6, Log-Lik: -4647.185, Max-Change: 0.02098Iteration: 7, Log-Lik: -4647.106, Max-Change: 0.01591Iteration: 8, Log-Lik: -4647.070, Max-Change: 0.01184Iteration: 9, Log-Lik: -4647.049, Max-Change: 0.00968Iteration: 10, Log-Lik: -4647.030, Max-Change: 0.01417Iteration: 11, Log-Lik: -4647.016, Max-Change: 0.00847Iteration: 12, Log-Lik: -4647.009, Max-Change: 0.00658Iteration: 13, Log-Lik: -4647.001, Max-Change: 0.01000Iteration: 14, Log-Lik: -4646.993, Max-Change: 0.00604Iteration: 15, Log-Lik: -4646.989, Max-Change: 0.00486Iteration: 16, Log-Lik: -4646.984, Max-Change: 0.00891Iteration: 17, Log-Lik: -4646.978, Max-Change: 0.00490Iteration: 18, Log-Lik: -4646.976, Max-Change: 0.00384Iteration: 19, Log-Lik: -4646.972, Max-Change: 0.00587Iteration: 20, Log-Lik: -4646.969, Max-Change: 0.00364Iteration: 21, Log-Lik: -4646.968, Max-Change: 0.00303Iteration: 22, Log-Lik: -4646.966, Max-Change: 0.00647Iteration: 23, Log-Lik: -4646.963, Max-Change: 0.00331Iteration: 24, Log-Lik: -4646.961, Max-Change: 0.00253Iteration: 25, Log-Lik: -4646.960, Max-Change: 0.00333Iteration: 26, Log-Lik: -4646.959, Max-Change: 0.00227Iteration: 27, Log-Lik: -4646.958, Max-Change: 0.00197Iteration: 28, Log-Lik: -4646.957, Max-Change: 0.00480Iteration: 29, Log-Lik: -4646.955, Max-Change: 0.00229Iteration: 30, Log-Lik: -4646.955, Max-Change: 0.00166Iteration: 31, Log-Lik: -4646.954, Max-Change: 0.00189Iteration: 32, Log-Lik: -4646.954, Max-Change: 0.00143Iteration: 33, Log-Lik: -4646.953, Max-Change: 0.00126Iteration: 34, Log-Lik: -4646.953, Max-Change: 0.00316Iteration: 35, Log-Lik: -4646.952, Max-Change: 0.00149Iteration: 36, Log-Lik: -4646.952, Max-Change: 0.00109Iteration: 37, Log-Lik: -4646.952, Max-Change: 0.00124Iteration: 38, Log-Lik: -4646.951, Max-Change: 0.00093Iteration: 39, Log-Lik: -4646.951, Max-Change: 0.00083Iteration: 40, Log-Lik: -4646.951, Max-Change: 0.00206Iteration: 41, Log-Lik: -4646.951, Max-Change: 0.00099Iteration: 42, Log-Lik: -4646.951, Max-Change: 0.00070Iteration: 43, Log-Lik: -4646.950, Max-Change: 0.00079Iteration: 44, Log-Lik: -4646.950, Max-Change: 0.00059Iteration: 45, Log-Lik: -4646.950, Max-Change: 0.00053Iteration: 46, Log-Lik: -4646.950, Max-Change: 0.00133Iteration: 47, Log-Lik: -4646.950, Max-Change: 0.00065Iteration: 48, Log-Lik: -4646.950, Max-Change: 0.00045Iteration: 49, Log-Lik: -4646.950, Max-Change: 0.00050Iteration: 50, Log-Lik: -4646.950, Max-Change: 0.00038Iteration: 51, Log-Lik: -4646.950, Max-Change: 0.00034Iteration: 52, Log-Lik: -4646.950, Max-Change: 0.00087Iteration: 53, Log-Lik: -4646.950, Max-Change: 0.00042Iteration: 54, Log-Lik: -4646.950, Max-Change: 0.00029Iteration: 55, Log-Lik: -4646.950, Max-Change: 0.00032Iteration: 56, Log-Lik: -4646.950, Max-Change: 0.00025Iteration: 57, Log-Lik: -4646.950, Max-Change: 0.00022Iteration: 58, Log-Lik: -4646.949, Max-Change: 0.00057Iteration: 59, Log-Lik: -4646.949, Max-Change: 0.00027Iteration: 60, Log-Lik: -4646.949, Max-Change: 0.00019Iteration: 61, Log-Lik: -4646.949, Max-Change: 0.00023Iteration: 62, Log-Lik: -4646.949, Max-Change: 0.00016Iteration: 63, Log-Lik: -4646.949, Max-Change: 0.00014Iteration: 64, Log-Lik: -4646.949, Max-Change: 0.00036Iteration: 65, Log-Lik: -4646.949, Max-Change: 0.00018Iteration: 66, Log-Lik: -4646.949, Max-Change: 0.00011Iteration: 67, Log-Lik: -4646.949, Max-Change: 0.00013Iteration: 68, Log-Lik: -4646.949, Max-Change: 0.00010Iteration: 69, Log-Lik: -4646.949, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_4, model = 1, group = group_4, 
    invariance = c("slopes", "intercepts", "free_var"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 69 EM iterations.
mirt version: 1.41 
M-step optimizer: nlminb 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4646.949
Estimated parameters: 50 
AIC = 9329.899
BIC = 9418.506; SABIC = 9361.337
G2 (747) = 807.87, p = 0.0604
RMSEA = 0.009, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
         F1    h2
auth1 0.510 0.260
auth2 0.644 0.415
auth3 0.663 0.440
auth4 0.536 0.287
auth5 0.534 0.285
auth6 0.591 0.349
auth7 0.653 0.426
auth8 0.536 0.287

SS loadings:  2.749 
Proportion Var:  0.344 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
         F1    h2
auth1 0.519 0.269
auth2 0.653 0.426
auth3 0.672 0.451
auth4 0.545 0.297
auth5 0.542 0.294
auth6 0.600 0.360
auth7 0.661 0.437
auth8 0.544 0.296

SS loadings:  2.831 
Proportion Var:  0.354 

Factor correlations: 

   F1
F1  1

----------
GROUP: Neither 
         F1    h2
auth1 0.648 0.420
auth2 0.771 0.594
auth3 0.786 0.618
auth4 0.674 0.454
auth5 0.671 0.451
auth6 0.725 0.526
auth7 0.778 0.605
auth8 0.673 0.454

SS loadings:  4.121 
Proportion Var:  0.515 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA    RMSEA_5   RMSEA_95  SRMSR.Boy SRMSR.Girl SRMSR.Neither       TLI
stats 301.9401 90 0 0.04819105 0.04223712 0.05421704 0.09622546 0.09622447    0.04661441 0.9172545
            CFI
stats 0.9113441
                      AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_scalar1_4    9329.899 9361.337 9363.551 9418.506 -4646.949             
mod_configural_4 9271.803 9355.638 9361.543 9508.090 -4587.901 118.096 30 0
Iteration: 1, Log-Lik: -4778.313, Max-Change: 0.43757Iteration: 2, Log-Lik: -4690.238, Max-Change: 0.25498Iteration: 3, Log-Lik: -4667.274, Max-Change: 0.14499Iteration: 4, Log-Lik: -4660.577, Max-Change: 0.08455Iteration: 5, Log-Lik: -4658.469, Max-Change: 0.04959Iteration: 6, Log-Lik: -4657.783, Max-Change: 0.02913Iteration: 7, Log-Lik: -4657.492, Max-Change: 0.01210Iteration: 8, Log-Lik: -4657.452, Max-Change: 0.00721Iteration: 9, Log-Lik: -4657.436, Max-Change: 0.00419Iteration: 10, Log-Lik: -4657.430, Max-Change: 0.00200Iteration: 11, Log-Lik: -4657.428, Max-Change: 0.00120Iteration: 12, Log-Lik: -4657.428, Max-Change: 0.00083Iteration: 13, Log-Lik: -4657.427, Max-Change: 0.00037Iteration: 14, Log-Lik: -4657.427, Max-Change: 0.00057Iteration: 15, Log-Lik: -4657.427, Max-Change: 0.00035Iteration: 16, Log-Lik: -4657.427, Max-Change: 0.00020Iteration: 17, Log-Lik: -4657.427, Max-Change: 0.00014Iteration: 18, Log-Lik: -4657.427, Max-Change: 0.00009

Call:
multipleGroup(data = irt_data_4, model = 1, group = group_4, 
    invariance = c("slopes", "intercepts"))

Full-information item factor analysis with 1 factor(s).
Converged within 1e-04 tolerance after 18 EM iterations.
mirt version: 1.41 
M-step optimizer: BFGS 
EM acceleration: Ramsay 
Number of rectangular quadrature: 61
Latent density type: Gaussian 

Log-likelihood = -4657.427
Estimated parameters: 48 
AIC = 9346.854
BIC = 9425.616; SABIC = 9374.799
G2 (749) = 828.82, p = 0.0222
RMSEA = 0.01, CFI = NaN, TLI = NaN

----------
GROUP: Boy 
         F1    h2
auth1 0.585 0.342
auth2 0.729 0.531
auth3 0.741 0.550
auth4 0.617 0.381
auth5 0.629 0.396
auth6 0.693 0.481
auth7 0.735 0.540
auth8 0.634 0.402

SS loadings:  3.621 
Proportion Var:  0.453 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
         F1    h2
auth1 0.585 0.342
auth2 0.729 0.531
auth3 0.741 0.550
auth4 0.617 0.381
auth5 0.629 0.396
auth6 0.693 0.481
auth7 0.735 0.540
auth8 0.634 0.402

SS loadings:  3.621 
Proportion Var:  0.453 

Factor correlations: 

   F1
F1  1

----------
GROUP: Neither 
         F1    h2
auth1 0.585 0.342
auth2 0.729 0.531
auth3 0.741 0.550
auth4 0.617 0.381
auth5 0.629 0.396
auth6 0.693 0.481
auth7 0.735 0.540
auth8 0.634 0.402

SS loadings:  3.621 
Proportion Var:  0.453 

Factor correlations: 

   F1
F1  1
            M2 df p      RMSEA   RMSEA_5   RMSEA_95 SRMSR.Boy SRMSR.Girl SRMSR.Neither       TLI
stats 331.2999 92 0 0.05064754 0.0448082 0.05655815 0.1289658  0.1205844     0.0707973 0.9086037
            CFI
stats 0.8998993
                         AIC    SABIC       HQ      BIC    logLik      X2 df p
mod_fullconstrain_4 9346.854 9374.799 9376.767 9425.616 -4657.427             
mod_configural_4    9271.803 9355.638 9361.543 9508.090 -4587.901 139.051 32 0
                      AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_metric_4     9311.400 9367.289 9371.227 9468.924 -4623.700            
mod_configural_4 9271.803 9355.638 9361.543 9508.090 -4587.901 71.597 16 0
                   AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_scalar2_4 9321.444 9356.375 9358.836 9419.897 -4640.722                
mod_metric_4  9311.400 9367.289 9371.227 9468.924 -4623.700 34.044 12 0.001
                   AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_scalar1_4 9329.899 9361.337 9363.551 9418.506 -4646.949                
mod_scalar2_4 9321.444 9356.375 9358.836 9419.897 -4640.722 12.455  2 0.002
                         AIC    SABIC       HQ      BIC    logLik     X2 df p
mod_fullconstrain_4 9346.854 9374.799 9376.767 9425.616 -4657.427            
mod_scalar1_4       9329.899 9361.337 9363.551 9418.506 -4646.949 20.955  2 0
                         AIC    SABIC       HQ      BIC    logLik     X2 df     p
mod_fullconstrain_4 9346.854 9374.799 9376.767 9425.616 -4657.427                
mod_scalar1_4       9329.899 9361.337 9363.551 9418.506 -4646.949 20.955  2     0
mod_scalar2_4       9321.444 9356.375 9358.836 9419.897 -4640.722 12.455  2 0.002
mod_metric_4        9311.400 9367.289 9371.227 9468.924 -4623.700 34.044 12 0.001
mod_configural_4    9271.803 9355.638 9361.543 9508.090 -4587.901 71.597 16     0

----------
GROUP: Boy 
         F1    h2
auth1 0.493 0.243
auth2 0.641 0.411
auth3 0.643 0.413
auth4 0.528 0.279
auth5 0.512 0.262
auth6 0.595 0.354
auth7 0.648 0.420
auth8 0.536 0.287

SS loadings:  2.669 
Proportion Var:  0.334 

Factor correlations: 

   F1
F1  1

----------
GROUP: Girl 
         F1    h2
auth1 0.508 0.258
auth2 0.656 0.431
auth3 0.658 0.433
auth4 0.544 0.296
auth5 0.527 0.278
auth6 0.611 0.373
auth7 0.663 0.440
auth8 0.551 0.304

SS loadings:  2.811 
Proportion Var:  0.351 

Factor correlations: 

   F1
F1  1

----------
GROUP: Neither 
         F1    h2
auth1 0.641 0.410
auth2 0.776 0.602
auth3 0.778 0.605
auth4 0.676 0.457
auth5 0.660 0.435
auth6 0.737 0.544
auth7 0.782 0.611
auth8 0.683 0.466

SS loadings:  4.13 
Proportion Var:  0.516 

Factor correlations: 

   F1
F1  1
$Boy
$items
         a1      d g u
auth1 0.963  0.581 0 1
auth2 1.422 -0.245 0 1
auth3 1.429  0.532 0 1
auth4 1.059 -0.590 0 1
auth5 1.014  1.507 0 1
auth6 1.260 -1.375 0 1
auth7 1.448 -0.176 0 1
auth8 1.079 -0.535 0 1

$means
F1 
 0 

$cov
   F1
F1  1


$Girl
$items
         a1      d g u
auth1 0.963  0.581 0 1
auth2 1.422 -0.245 0 1
auth3 1.429  0.532 0 1
auth4 1.059 -0.590 0 1
auth5 1.014  1.507 0 1
auth6 1.260 -1.375 0 1
auth7 1.448 -0.176 0 1
auth8 1.079 -0.535 0 1

$means
    F1 
-0.101 

$cov
      F1
F1 1.084


$Neither
$items
         a1      d g u
auth1 0.963  0.581 0 1
auth2 1.422 -0.245 0 1
auth3 1.429  0.532 0 1
auth4 1.059 -0.590 0 1
auth5 1.014  1.507 0 1
auth6 1.260 -1.375 0 1
auth7 1.448 -0.176 0 1
auth8 1.079 -0.535 0 1

$means
    F1 
-0.358 

$cov
      F1
F1 2.172


$Boy
       auth1  auth2  auth3  auth4  auth5  auth6  auth7  auth8
auth1     NA -0.208  0.219 -0.205  0.142 -0.311 -0.230 -0.226
auth2 10.805     NA -0.262 -0.104  0.141  0.136  0.131  0.158
auth3 11.982 17.097     NA  0.208  0.210 -0.258 -0.255 -0.292
auth4 10.511  2.705 10.806     NA -0.160 -0.100 -0.100 -0.141
auth5  5.029  4.961 10.960  6.364     NA -0.150 -0.183  0.182
auth6 24.065  4.633 16.555  2.493  5.605     NA  0.178  0.172
auth7 13.184  4.267 16.208  2.483  8.329  7.922     NA -0.128
auth8 12.739  6.224 21.218  4.932  8.240  7.409  4.103     NA

$Girl
       auth1  auth2  auth3  auth4  auth5  auth6  auth7  auth8
auth1     NA -0.141  0.160  0.115 -0.190 -0.346 -0.133 -0.267
auth2  2.921     NA -0.129 -0.083 -0.176  0.192 -0.098 -0.131
auth3  3.773  2.445     NA  0.107  0.224  0.182 -0.177  0.150
auth4  1.929  1.018  1.696     NA -0.190  0.169 -0.243 -0.129
auth5  5.288  4.550  7.359  5.291     NA -0.326  0.190 -0.237
auth6 17.601  5.402  4.873  4.184 15.650     NA  0.221  0.354
auth7  2.601  1.425  4.591  8.672  5.335  7.188     NA  0.191
auth8 10.450  2.540  3.314  2.444  8.244 18.391  5.343     NA

$Neither
       auth1 auth2  auth3  auth4  auth5  auth6  auth7  auth8
auth1     NA 0.154  0.135  0.086  0.131 -0.128  0.099  0.111
auth2 14.592    NA -0.079 -0.024 -0.089 -0.102  0.023 -0.067
auth3 11.292 3.892     NA  0.083  0.112  0.124  0.071 -0.123
auth4  4.560 0.354  4.263     NA  0.082  0.090  0.013 -0.075
auth5 10.700 4.863  7.714  4.174     NA  0.121  0.092  0.134
auth6 10.155 6.379  9.536  5.043  9.120     NA -0.080  0.090
auth7  6.102 0.325  3.099  0.112  5.213  3.950     NA -0.080
auth8  7.642 2.738  9.422  3.437 11.095  5.012  3.922     NA

# weights:  45 (28 variable)
initial  value 995.342734 
iter  10 value 805.567272
iter  20 value 756.357697
iter  30 value 723.178126
final  value 721.617400 
converged

==============================================
                      Dependent variable:     
                  ----------------------------
                       Boy           Girl     
                       (1)            (2)     
----------------------------------------------
daughter            -0.854***      1.657***   
                     (0.184)        (0.273)   
                                              
age                  -0.148**      -0.305***  
                     (0.059)        (0.070)   
                                              
male                 0.839***       -0.165    
                     (0.189)        (0.217)   
                                              
white                 -0.050         0.294    
                     (0.219)        (0.265)   
                                              
latino               0.646**         0.341    
                     (0.254)        (0.297)   
                                              
education             -0.015        -0.026    
                     (0.069)        (0.082)   
                                              
income                0.074          0.028    
                     (0.067)        (0.079)   
                                              
bornagain            0.614***        0.335    
                     (0.189)        (0.215)   
                                              
pid                  0.256**        -0.083    
                     (0.116)        (0.134)   
                                              
hostile_sexism        0.020          0.003    
                     (0.016)        (0.018)   
                                              
hostil_men            0.014          0.054    
                     (0.029)        (0.035)   
                                              
ben_men              0.096***       0.078*    
                     (0.034)        (0.041)   
                                              
ben_sexism            -0.004        -0.009    
                     (0.013)        (0.016)   
                                              
Constant            -2.776***      -3.078***  
                     (0.651)        (0.790)   
                                              
----------------------------------------------
Akaike Inf. Crit.   1,499.235      1,499.235  
==============================================
Note:              *p<0.1; **p<0.05; ***p<0.01

  1   2 
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